From 20066028b833a47fc91d92d5dfc5b1cfe3c06ddc Mon Sep 17 00:00:00 2001 From: napo Date: Fri, 14 Apr 2023 11:27:08 +0200 Subject: [PATCH] update italian translation --- pages/models/chatgpt.it.mdx | 140 ++++++++-------- pages/models/gpt-4.it.mdx | 22 +-- pages/models/llama.it.mdx | 14 +- pages/papers.it.mdx | 314 ++++++++++++++++++------------------ pages/techniques/ape.it.mdx | 4 +- pages/techniques/cot.it.mdx | 4 +- pages/techniques/dsp.it.mdx | 2 +- 7 files changed, 250 insertions(+), 250 deletions(-) diff --git a/pages/models/chatgpt.it.mdx b/pages/models/chatgpt.it.mdx index fe7f894..f2ca220 100644 --- a/pages/models/chatgpt.it.mdx +++ b/pages/models/chatgpt.it.mdx @@ -149,76 +149,76 @@ La raccomandazione attuale per `gpt-3.5-turbo-0301` è di aggiungere le istruzio --- ## Referenze -- [Large language models can rate news outlet credibility](https://arxiv.org/abs/2304.00228) (April 2023) -- [Can AI Chatbots Pass the Fundamentals of Engineering (FE) and Principles and Practice of Engineering (PE) Structural Exams?](https://arxiv.org/abs/2303.18149) (April 2023) -- [Can AI Put Gamma-Ray Astrophysicists Out of a Job?](https://arxiv.org/abs/2303.17853) (March 2023) -- [Comparing Abstractive Summaries Generated by ChatGPT to Real Summaries Through Blinded Reviewers and Text Classification Algorithms](https://arxiv.org/abs/2303.17650) (March 2023) -- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace](https://arxiv.org/abs/2303.17580) (March 2023) -- [WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research](https://arxiv.org/abs/2303.17395) (March 2023) -- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://arxiv.org/abs/2303.17466) (March 2023) -- [Yes but.. Can ChatGPT Identify Entities in Historical Documents?](https://arxiv.org/abs/2303.17322) (March 2023) -- [Evaluation of ChatGPT for NLP-based Mental Health Applications](https://arxiv.org/abs/2303.15727) (March 2023) -- [A Perspectival Mirror of the Elephant: Investigating Language Bias on Google, ChatGPT, Wikipedia, and YouTube](https://arxiv.org/abs/2303.16281) (March 2023) -- [ChatGPT or academic scientist? Distinguishing authorship with over 99% accuracy using off-the-shelf machine learning tools](https://arxiv.org/abs/2303.16352) (March 2023) -- [Zero-shot Clinical Entity Recognition using ChatGPT](https://arxiv.org/abs/2303.16416) (March 2023) -- [ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models](https://arxiv.org/abs/2303.16421) (March 2023) -- [ChatGPT4PCG Competition: Character-like Level Generation for Science Birds](https://arxiv.org/abs/2303.15662) (March 2023) -- [ChatGPT as a Factual Inconsistency Evaluator for Abstractive Text Summarization](https://arxiv.org/abs/2303.15621) (March 2023) -- [Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System](https://arxiv.org/abs/2303.14524) (March 2023) -- [A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability](https://arxiv.org/abs/2303.13547) (March 2023) -- [Towards Making the Most of ChatGPT for Machine Translation](https://arxiv.org/abs/2303.13780) (March 2023) -- [Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models: A Case Study on ChatGPT](https://arxiv.org/abs/2303.13809) (March 2023) -- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://arxiv.org/pdf/2303.15056v1.pdf) (March 2023) -- [ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark](https://arxiv.org/abs/2303.13648) (March 2023) -- [ChatGPT and a New Academic Reality: AI-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishing](https://arxiv.org/abs/2303.13367) (March 2023) -- [Are LLMs the Master of All Trades? : Exploring Domain-Agnostic Reasoning Skills of LLMs](https://arxiv.org/abs/2303.12810) (March 2023) -- [Is ChatGPT A Good Keyphrase Generator? A Preliminary Study](https://arxiv.org/abs/2303.13001) (March 2023) -- [MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action](https://arxiv.org/abs/2303.11381) (March 2023) -- [Large Language Models Can Be Used to Estimate the Ideologies of Politicians in a Zero-Shot Learning Setting](https://arxiv.org/abs/2303.12057) (March 2023) -- [Chinese Intermediate English Learners outdid ChatGPT in deep cohesion: Evidence from English narrative writing](https://arxiv.org/abs/2303.11812) (March 2023) -- [A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models](https://arxiv.org/abs/2303.10420) (March 2023) -- [ChatGPT as the Transportation Equity Information Source for Scientific Writing](https://arxiv.org/abs/2303.11158) (March 2023) -- [Translating Radiology Reports into Plain Language using ChatGPT and GPT-4 with Prompt Learning: Promising Results, Limitations, and Potential](https://arxiv.org/abs/2303.09038) (March 2023) -- [ChatGPT Participates in a Computer Science Exam](https://arxiv.org/abs/2303.09461) (March 2023) -- [Consistency Analysis of ChatGPT](https://arxiv.org/abs/2303.06273) (Mar 2023) -- [Algorithmic Ghost in the Research Shell: Large Language Models and Academic Knowledge Creation in Management Research](https://arxiv.org/abs/2303.07304) (Mar 2023) -- [Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification](https://arxiv.org/abs/2303.07142) (March 2023) -- [Seeing ChatGPT Through Students' Eyes: An Analysis of TikTok Data](https://arxiv.org/abs/2303.05349) (March 2023) -- [Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering -- Example of ChatGPT](https://arxiv.org/abs/2303.05352) (Mar 2023) -- [ChatGPT is on the horizon: Could a large language model be all we need for Intelligent Transportation?](https://arxiv.org/abs/2303.05382) (Mar 2023) -- [Making a Computational Attorney](https://arxiv.org/abs/2303.05383) (Mar 2023) -- [Does Synthetic Data Generation of LLMs Help Clinical Text Mining?](https://arxiv.org/abs/2303.04360) (Mar 2023) -- [MenuCraft: Interactive Menu System Design with Large Language Models](https://arxiv.org/abs/2303.04496) (Mar 2023) -- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://arxiv.org/abs/2303.04226) (Mar 2023) +- [Large language models can rate news outlet credibility](https://arxiv.org/abs/2304.00228) (Aprile 2023) +- [Can AI Chatbots Pass the Fundamentals of Engineering (FE) and Principles and Practice of Engineering (PE) Structural Exams?](https://arxiv.org/abs/2303.18149) (Aprile 2023) +- [Can AI Put Gamma-Ray Astrophysicists Out of a Job?](https://arxiv.org/abs/2303.17853) (Marzo 2023) +- [Comparing Abstractive Summaries Generated by ChatGPT to Real Summaries Through Blinded Reviewers and Text Classification Algorithms](https://arxiv.org/abs/2303.17650) (Marzo 2023) +- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace](https://arxiv.org/abs/2303.17580) (Marzo 2023) +- [WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research](https://arxiv.org/abs/2303.17395) (Marzo 2023) +- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://arxiv.org/abs/2303.17466) (Marzo 2023) +- [Yes but.. Can ChatGPT Identify Entities in Historical Documents?](https://arxiv.org/abs/2303.17322) (Marzo 2023) +- [Evaluation of ChatGPT for NLP-based Mental Health Applications](https://arxiv.org/abs/2303.15727) (Marzo 2023) +- [A Perspectival Mirror of the Elephant: Investigating Language Bias on Google, ChatGPT, Wikipedia, and YouTube](https://arxiv.org/abs/2303.16281) (Marzo 2023) +- [ChatGPT or academic scientist? Distinguishing authorship with over 99% accuracy using off-the-shelf machine learning tools](https://arxiv.org/abs/2303.16352) (Marzo 2023) +- [Zero-shot Clinical Entity Recognition using ChatGPT](https://arxiv.org/abs/2303.16416) (Marzo 2023) +- [ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models](https://arxiv.org/abs/2303.16421) (Marzo 2023) +- [ChatGPT4PCG Competition: Character-like Level Generation for Science Birds](https://arxiv.org/abs/2303.15662) (Marzo 2023) +- [ChatGPT as a Factual Inconsistency Evaluator for Abstractive Text Summarization](https://arxiv.org/abs/2303.15621) (Marzo 2023) +- [Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System](https://arxiv.org/abs/2303.14524) (Marzo 2023) +- [A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability](https://arxiv.org/abs/2303.13547) (Marzo 2023) +- [Towards Making the Most of ChatGPT for Machine Translation](https://arxiv.org/abs/2303.13780) (Marzo 2023) +- [Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models: A Case Study on ChatGPT](https://arxiv.org/abs/2303.13809) (Marzo 2023) +- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://arxiv.org/pdf/2303.15056v1.pdf) (Marzo 2023) +- [ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark](https://arxiv.org/abs/2303.13648) (Marzo 2023) +- [ChatGPT and a New Academic Reality: AI-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishing](https://arxiv.org/abs/2303.13367) (Marzo 2023) +- [Are LLMs the Master of All Trades? : Exploring Domain-Agnostic Reasoning Skills of LLMs](https://arxiv.org/abs/2303.12810) (Marzo 2023) +- [Is ChatGPT A Good Keyphrase Generator? A Preliminary Study](https://arxiv.org/abs/2303.13001) (Marzo 2023) +- [MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action](https://arxiv.org/abs/2303.11381) (Marzo 2023) +- [Large Language Models Can Be Used to Estimate the Ideologies of Politicians in a Zero-Shot Learning Setting](https://arxiv.org/abs/2303.12057) (Marzo 2023) +- [Chinese Intermediate English Learners outdid ChatGPT in deep cohesion: Evidence from English narrative writing](https://arxiv.org/abs/2303.11812) (Marzo 2023) +- [A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models](https://arxiv.org/abs/2303.10420) (Marzo 2023) +- [ChatGPT as the Transportation Equity Information Source for Scientific Writing](https://arxiv.org/abs/2303.11158) (Marzo 2023) +- [Translating Radiology Reports into Plain Language using ChatGPT and GPT-4 with Prompt Learning: Promising Results, Limitations, and Potential](https://arxiv.org/abs/2303.09038) (Marzo 2023) +- [ChatGPT Participates in a Computer Science Exam](https://arxiv.org/abs/2303.09461) (Marzo 2023) +- [Consistency Analysis of ChatGPT](https://arxiv.org/abs/2303.06273) (Marzo 2023) +- [Algorithmic Ghost in the Research Shell: Large Language Models and Academic Knowledge Creation in Management Research](https://arxiv.org/abs/2303.07304) (Marzo 2023) +- [Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification](https://arxiv.org/abs/2303.07142) (Marzo 2023) +- [Seeing ChatGPT Through Students' Eyes: An Analysis of TikTok Data](https://arxiv.org/abs/2303.05349) (Marzo 2023) +- [Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering -- Example of ChatGPT](https://arxiv.org/abs/2303.05352) (Marzo 2023) +- [ChatGPT is on the horizon: Could a large language model be all we need for Intelligent Transportation?](https://arxiv.org/abs/2303.05382) (Marzo 2023) +- [Making a Computational Attorney](https://arxiv.org/abs/2303.05383) (Marzo 2023) +- [Does Synthetic Data Generation of LLMs Help Clinical Text Mining?](https://arxiv.org/abs/2303.04360) (Marzo 2023) +- [MenuCraft: Interactive Menu System Design with Large Language Models](https://arxiv.org/abs/2303.04496) (Marzo 2023) +- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://arxiv.org/abs/2303.04226) (Marzo 2023) - [Exploring the Feasibility of ChatGPT for Event Extraction](https://arxiv.org/abs/2303.03836) -- [ChatGPT: Beginning of an End of Manual Annotation? Use Case of Automatic Genre Identification](https://arxiv.org/abs/2303.03953) (Mar 2023) -- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://arxiv.org/abs/2303.04048) (Mar 2023) -- [Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT](https://arxiv.org/abs/2303.03186) (Mar 2023) -- [UZH_CLyp at SemEval-2023 Task 9: Head-First Fine-Tuning and ChatGPT Data Generation for Cross-Lingual Learning in Tweet Intimacy Prediction](https://arxiv.org/abs/2303.01194) (Mar 2023) -- [How to format inputs to ChatGPT models](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb) (Mar 2023) -- [Can ChatGPT Assess Human Personalities? A General Evaluation Framework](https://arxiv.org/abs/2303.01248) (Mar 2023) -- [Cross-Lingual Summarization via ChatGPT](https://arxiv.org/abs/2302.14229) (Feb 2023) -- [ChatAug: Leveraging ChatGPT for Text Data Augmentation](https://arxiv.org/abs/2302.13007) (Feb 2023) -- [Dr ChatGPT, tell me what I want to hear: How prompt knowledge impacts health answer correctness](https://arxiv.org/abs/2302.13793) (Feb 2023) -- [An Independent Evaluation of ChatGPT on Mathematical Word Problems (MWP)](https://arxiv.org/abs/2302.13814) (Feb 2023) -- [ChatGPT: A Meta-Analysis after 2.5 Months](https://arxiv.org/abs/2302.13795) (Feb 2023) -- [Let's have a chat! A Conversation with ChatGPT: Technology, Applications, and Limitations](https://arxiv.org/abs/2302.13817) (Feb 2023) -- [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://arxiv.org/abs/2302.12813) (Feb 2023) -- [On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective](https://arxiv.org/abs/2302.12095) (Feb 2023) -- [How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study](https://arxiv.org/abs/2302.10916) (Feb 2023) -- [Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT](https://arxiv.org/abs/2302.10198) (Feb 2023) -- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://arxiv.org/abs/2302.11382) (Feb 2023) -- [Zero-Shot Information Extraction via Chatting with ChatGPT](https://arxiv.org/abs/2302.10205) (Feb 2023) -- [ChatGPT: Jack of all trades, master of none](https://arxiv.org/abs/2302.10724) (Feb 2023) -- [A Pilot Evaluation of ChatGPT and DALL-E 2 on Decision Making and Spatial Reasoning](https://arxiv.org/abs/2302.09068) (Feb 2023) -- [Netizens, Academicians, and Information Professionals' Opinions About AI With Special Reference To ChatGPT](https://arxiv.org/abs/2302.07136) (Feb 2023) -- [Linguistic ambiguity analysis in ChatGPT](https://arxiv.org/abs/2302.06426) (Feb 2023) -- [ChatGPT versus Traditional Question Answering for Knowledge Graphs: Current Status and Future Directions Towards Knowledge Graph Chatbots](https://arxiv.org/abs/2302.06466) (Feb 2023) -- [What ChatGPT and generative AI mean for science](https://www.nature.com/articles/d41586-023-00340-6) (Feb 2023) -- [Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature](https://arxiv.org/abs/2302.06474) (Feb 2023) -- [Exploring AI Ethics of ChatGPT: A Diagnostic Analysis](https://arxiv.org/abs/2301.12867) (Jan 2023) -- [ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education](https://www.edu.sot.tum.de/fileadmin/w00bed/hctl/_my_direct_uploads/ChatGPT_for_Good_.pdf) (Jan 2023) -- [The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation](https://arxiv.org/abs/2301.01768) (Jan 2023) +- [ChatGPT: Beginning of an End of Manual Annotation? Use Case of Automatic Genre Identification](https://arxiv.org/abs/2303.03953) (Marzo 2023) +- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://arxiv.org/abs/2303.04048) (Marzo 2023) +- [Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT](https://arxiv.org/abs/2303.03186) (Marzo 2023) +- [UZH_CLyp at SemEval-2023 Task 9: Head-First Fine-Tuning and ChatGPT Data Generation for Cross-Lingual Learning in Tweet Intimacy Prediction](https://arxiv.org/abs/2303.01194) (Marzo 2023) +- [How to format inputs to ChatGPT models](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb) (Marzo 2023) +- [Can ChatGPT Assess Human Personalities? A General Evaluation Framework](https://arxiv.org/abs/2303.01248) (Marzo 2023) +- [Cross-Lingual Summarization via ChatGPT](https://arxiv.org/abs/2302.14229) (Febbraio 2023) +- [ChatAug: Leveraging ChatGPT for Text Data Augmentation](https://arxiv.org/abs/2302.13007) (Febbraio 2023) +- [Dr ChatGPT, tell me what I want to hear: How prompt knowledge impacts health answer correctness](https://arxiv.org/abs/2302.13793) (Febbraio 2023) +- [An Independent Evaluation of ChatGPT on Mathematical Word Problems (MWP)](https://arxiv.org/abs/2302.13814) (Febbraio 2023) +- [ChatGPT: A Meta-Analysis after 2.5 Months](https://arxiv.org/abs/2302.13795) (Febbraio 2023) +- [Let's have a chat! A Conversation with ChatGPT: Technology, Applications, and Limitations](https://arxiv.org/abs/2302.13817) (Febbraio 2023) +- [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://arxiv.org/abs/2302.12813) (Febbraio 2023) +- [On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective](https://arxiv.org/abs/2302.12095) (Febbraio 2023) +- [How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study](https://arxiv.org/abs/2302.10916) (Febbraio 2023) +- [Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT](https://arxiv.org/abs/2302.10198) (Febbraio 2023) +- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://arxiv.org/abs/2302.11382) (Febbraio 2023) +- [Zero-Shot Information Extraction via Chatting with ChatGPT](https://arxiv.org/abs/2302.10205) (Febbraio 2023) +- [ChatGPT: Jack of all trades, master of none](https://arxiv.org/abs/2302.10724) (Febbraio 2023) +- [A Pilot Evaluation of ChatGPT and DALL-E 2 on Decision Making and Spatial Reasoning](https://arxiv.org/abs/2302.09068) (Febbraio 2023) +- [Netizens, Academicians, and Information Professionals' Opinions About AI With Special Reference To ChatGPT](https://arxiv.org/abs/2302.07136) (Febbraio 2023) +- [Linguistic ambiguity analysis in ChatGPT](https://arxiv.org/abs/2302.06426) (Febbraio 2023) +- [ChatGPT versus Traditional Question Answering for Knowledge Graphs: Current Status and Future Directions Towards Knowledge Graph Chatbots](https://arxiv.org/abs/2302.06466) (Febbraio 2023) +- [What ChatGPT and generative AI mean for science](https://www.nature.com/articles/d41586-023-00340-6) (Febbraio 2023) +- [Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature](https://arxiv.org/abs/2302.06474) (Febbraio 2023) +- [Exploring AI Ethics of ChatGPT: A Diagnostic Analysis](https://arxiv.org/abs/2301.12867) (Gennaio 2023) +- [ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education](https://www.edu.sot.tum.de/fileadmin/w00bed/hctl/_my_direct_uploads/ChatGPT_for_Good_.pdf) (Gennaio 2023) +- [The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation](https://arxiv.org/abs/2301.01768) (Gennaio 2023) - [Techniques to improve reliability - OpenAI Cookbook](https://github.com/openai/openai-cookbook/blob/main/techniques_to_improve_reliability.md) - [Awesome ChatGPT Prompts](https://github.com/f/awesome-chatgpt-prompts) -- [Introducing ChatGPT](https://openai.com/blog/chatgpt) (Nov 2022) +- [Introducing ChatGPT](https://openai.com/blog/chatgpt) (Novembre 2022) diff --git a/pages/models/gpt-4.it.mdx b/pages/models/gpt-4.it.mdx index a3ca3de..2077297 100644 --- a/pages/models/gpt-4.it.mdx +++ b/pages/models/gpt-4.it.mdx @@ -159,14 +159,14 @@ Prossimamente! ## Referenze / Articoli scientifici -- [Evaluating GPT-4 and ChatGPT on Japanese Medical Licensing Examinations](https://arxiv.org/abs/2303.18027) (April 2023) -- [Evaluation of GPT and BERT-based models on identifying protein-protein interactions in biomedical text]() (March 2023) -- [Evaluating GPT-3.5 and GPT-4 Models on Brazilian University Admission Exams](https://arxiv.org/abs/2303.17003) (March 2023) -- [GPTEval: NLG Evaluation using GPT-4 with Better Human Alignment](https://arxiv.org/abs/2303.16634) (March 2023) -- [Humans in Humans Out: On GPT Converging Toward Common Sense in both Success and Failure](https://arxiv.org/abs/2303.17276) (March 2023) -- [GPT is becoming a Turing machine: Here are some ways to program it](https://arxiv.org/abs/2303.14310) (March 2023) -- [Mind meets machine: Unravelling GPT-4's cognitive psychology](https://arxiv.org/abs/2303.11436) (March 2023) -- [Capabilities of GPT-4 on Medical Challenge Problems](https://www.microsoft.com/en-us/research/uploads/prod/2023/03/GPT-4_medical_benchmarks.pdf) (March 2023) -- [GPT-4 Technical Report](https://cdn.openai.com/papers/gpt-4.pdf) (March 2023) -- [DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4](https://arxiv.org/abs/2303.11032) (March 2023) -- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://arxiv.org/abs/2303.10130) (March 2023) +- [Evaluating GPT-4 and ChatGPT on Japanese Medical Licensing Examinations](https://arxiv.org/abs/2303.18027) (Aprile 2023) +- [Evaluation of GPT and BERT-based models on identifying protein-protein interactions in biomedical text]() (Marzo 2023) +- [Evaluating GPT-3.5 and GPT-4 Models on Brazilian University Admission Exams](https://arxiv.org/abs/2303.17003) (Marzo 2023) +- [GPTEval: NLG Evaluation using GPT-4 with Better Human Alignment](https://arxiv.org/abs/2303.16634) (Marzo 2023) +- [Humans in Humans Out: On GPT Converging Toward Common Sense in both Success and Failure](https://arxiv.org/abs/2303.17276) (Marzo 2023) +- [GPT is becoming a Turing machine: Here are some ways to program it](https://arxiv.org/abs/2303.14310) (Marzo 2023) +- [Mind meets machine: Unravelling GPT-4's cognitive psychology](https://arxiv.org/abs/2303.11436) (Marzo 2023) +- [Capabilities of GPT-4 on Medical Challenge Problems](https://www.microsoft.com/en-us/research/uploads/prod/2023/03/GPT-4_medical_benchmarks.pdf) (Marzo 2023) +- [GPT-4 Technical Report](https://cdn.openai.com/papers/gpt-4.pdf) (Marzo 2023) +- [DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4](https://arxiv.org/abs/2303.11032) (Marzo 2023) +- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://arxiv.org/abs/2303.10130) (Marzo 2023) diff --git a/pages/models/llama.it.mdx b/pages/models/llama.it.mdx index 46b70e9..8004450 100644 --- a/pages/models/llama.it.mdx +++ b/pages/models/llama.it.mdx @@ -33,10 +33,10 @@ Nel complesso, LLaMA-13B supera GPT-3 (175B) su molti benchmark nonostante sia 1 ## Referenze -- [Koala: A Dialogue Model for Academic Research](https://bair.berkeley.edu/blog/2023/04/03/koala/) (April 2023) -- [Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data](https://arxiv.org/abs/2304.01196) (April 2023) -- [Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality](https://vicuna.lmsys.org/) (March 2023) -- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://arxiv.org/abs/2303.16199) (March 2023) -- [GPT4All](https://github.com/nomic-ai/gpt4all) (March 2023) -- [ChatDoctor: A Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge](https://arxiv.org/abs/2303.14070) (March 2023) -- [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) (March 2023) +- [Koala: A Dialogue Model for Academic Research](https://bair.berkeley.edu/blog/2023/04/03/koala/) (Aprile 2023) +- [Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data](https://arxiv.org/abs/2304.01196) (Aprile 2023) +- [Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality](https://vicuna.lmsys.org/) (Marzo 2023) +- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://arxiv.org/abs/2303.16199) (Marzo 2023) +- [GPT4All](https://github.com/nomic-ai/gpt4all) (Marzo 2023) +- [ChatDoctor: A Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge](https://arxiv.org/abs/2303.14070) (Marzo 2023) +- [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) (Marzo 2023) diff --git a/pages/papers.it.mdx b/pages/papers.it.mdx index 87a5747..7ef1cac 100644 --- a/pages/papers.it.mdx +++ b/pages/papers.it.mdx @@ -5,172 +5,172 @@ Di seguito sono riportati gli articoli scientifici più recenti (ordinati in bas ## Panoramica - - [A Survey of Large Language Models](https://arxiv.org/abs/2303.18223) (April 2023) - - [Nature Language Reasoning, A Survey](https://arxiv.org/abs/2303.14725) (Mar 2023) - - [Augmented Language Models: a Survey](https://arxiv.org/abs/2302.07842) (Feb 2023) - - [A Survey for In-context Learning](https://arxiv.org/abs/2301.00234) (Dec 2022) - - [Towards Reasoning in Large Language Models: A Survey](https://arxiv.org/abs/2212.10403) (Dec 2022) - - [Reasoning with Language Model Prompting: A Survey](https://arxiv.org/abs/2212.09597) (Dec 2022) - - [Emergent Abilities of Large Language Models](https://arxiv.org/abs/2206.07682) (Jun 2022) - - [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) (Apr 2022) + - [A Survey of Large Language Models](https://arxiv.org/abs/2303.18223) (Aprile 2023) + - [Nature Language Reasoning, A Survey](https://arxiv.org/abs/2303.14725) (Marzo 2023) + - [Augmented Language Models: a Survey](https://arxiv.org/abs/2302.07842) (Febbraio 2023) + - [A Survey for In-context Learning](https://arxiv.org/abs/2301.00234) (Dicembre 2022) + - [Towards Reasoning in Large Language Models: A Survey](https://arxiv.org/abs/2212.10403) (Dicembre 2022) + - [Reasoning with Language Model Prompting: A Survey](https://arxiv.org/abs/2212.09597) (Dicembre 2022) + - [Emergent Abilities of Large Language Models](https://arxiv.org/abs/2206.07682) (Giugno 2022) + - [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) (Aprile 2022) - [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://arxiv.org/abs/2107.13586) (Jul 2021) ## Approcci - - [CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society](https://arxiv.org/abs/2303.17760) (Mar 2023) - - [Self-Refine: Iterative Refinement with Self-Feedback](https://arxiv.org/abs/2303.17651v1) (Mar 2023) - - [kNN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference](https://arxiv.org/abs/2303.13824) (Mar 2023) - - [Visual-Language Prompt Tuning with Knowledge-guided Context Optimization](https://arxiv.org/abs/2303.13283) (Mar 2023) - - [Fairness-guided Few-shot Prompting for Large Language Models](https://arxiv.org/abs/2303.13217) (Mar 2023) - - [Context-faithful Prompting for Large Language Models](https://arxiv.org/abs/2303.11315) (Mar 2023) - - [Is Prompt All You Need? No. A Comprehensive and Broader View of Instruction Learning](https://arxiv.org/abs/2303.10475) (Mar 2023) - - [UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation](https://arxiv.org/abs/2303.08518) (Mar 2023) - - [Model-tuning Via Prompts Makes NLP Models Adversarially Robust](https://arxiv.org/abs/2303.07320) (Mar 2023) - - [Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer](https://arxiv.org/abs/2303.03922) (March 2023) - - [CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification](https://arxiv.org/abs/2303.03628) (March 2023) - - [Larger language models do in-context learning differently](https://arxiv.org/abs/2303.03846) (March 2023) - - [OpenICL: An Open-Source Framework for In-context Learning](https://arxiv.org/abs/2303.02913) (March 2023) - - [Dynamic Prompting: A Unified Framework for Prompt Tuning](https://arxiv.org/abs/2303.02909) (March 2023) - - [Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning](https://arxiv.org/abs/2303.02861) (March 2023) - - [Effectiveness of Data Augmentation for Prefix Tuning with Limited Data](https://arxiv.org/abs/2303.02577) (March 2023) - - [Mixture of Soft Prompts for Controllable Data Generation](https://arxiv.org/abs/2303.01580) (March 2023) - - [Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners](https://arxiv.org/abs/2303.02151) (March 2023) - - [How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks](https://arxiv.org/abs/2303.00293) (March 2023) - - [Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT](https://arxiv.org/pdf/2302.10198.pdf) (Feb 2023) - - [EvoPrompting: Language Models for Code-Level Neural Architecture Search](https://arxiv.org/abs/2302.14838) (Feb 2023) - - [In-Context Instruction Learning](https://arxiv.org/abs/2302.14691) (Feb 2023) - - [Chain of Hindsight Aligns Language Models with Feedback](https://arxiv.org/abs/2302.02676) (Feb 2023) - - [Language Is Not All You Need: Aligning Perception with Language Models](https://arxiv.org/abs/2302.14045) (Feb 2023) - - [Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data](https://arxiv.org/abs/2302.12822) (Feb 2023) - - [Active Prompting with Chain-of-Thought for Large Language Models](https://arxiv.org/abs/2302.12246) (Feb 2023) - - [More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models](https://arxiv.org/abs/2302.12173) (Feb 2023) - - [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://arxiv.org/abs/2302.11382) (Feb 2023) - - [Guiding Large Language Models via Directional Stimulus Prompting](https://arxiv.org/abs/2302.11520) (Feb 2023) - - [How Does In-Context Learning Help Prompt Tuning?](https://arxiv.org/abs/2302.11521) (Feb 2023) - - [Scalable Prompt Generation for Semi-supervised Learning with Language Models](https://arxiv.org/abs/2302.09236) (Feb 2023) - - [Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints](https://arxiv.org/abs/2302.09185) (Feb 2023) - - [À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting](https://arxiv.org/abs/2302.07994) (Feb 2023) - - [GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks](https://arxiv.org/abs/2302.08043) (Feb 2023) - - [The Capacity for Moral Self-Correction in Large Language Models](https://arxiv.org/abs/2302.07459) (Feb 2023) - - [SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains](https://arxiv.org/abs/2302.06868) (Feb 2023) - - [Evaluating the Robustness of Discrete Prompts](https://arxiv.org/abs/2302.05619) (Feb 2023) - - [Compositional Exemplars for In-context Learning](https://arxiv.org/abs/2302.05698) (Feb 2023) - - [Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery](https://arxiv.org/abs/2302.03668) (Feb 2023) - - [Multimodal Chain-of-Thought Reasoning in Language Models](https://arxiv.org/abs/2302.00923) (Feb 2023) - - [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://arxiv.org/abs/2302.00093) (Feb 2023) - - [Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models](https://arxiv.org/abs/2302.00618) (Feb 2023) - - [Progressive Prompts: Continual Learning for Language Models](https://arxiv.org/abs/2301.12314) (Jan 2023) - - [Batch Prompting: Efficient Inference with LLM APIs](https://arxiv.org/abs/2301.08721) (Jan 2023) - - [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP](https://arxiv.org/abs/2212.14024) (Dec 2022) - - [On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://arxiv.org/abs/2212.08061) (Dec 2022) - - [Constitutional AI: Harmlessness from AI Feedback](https://arxiv.org/abs/2212.08073) (Dec 2022) - - [Successive Prompting for Decomposing Complex Questions](https://arxiv.org/abs/2212.04092) (Dec 2022) - - [Large Language Models are reasoners with Self-Verification](https://arxiv.org/abs/2212.09561v1) (Dec 2022) - - [Discovering Language Model Behaviors with Model-Written Evaluations](https://arxiv.org/abs/2212.09251) (Dec 2022) - - [Structured Prompting: Scaling In-Context Learning to 1,000 Examples](https://arxiv.org/abs/2212.06713) (Dec 2022) - - [PAL: Program-aided Language Models](https://arxiv.org/abs/2211.10435) (Nov 2022) - - [Large Language Models Are Human-Level Prompt Engineers](https://arxiv.org/abs/2211.01910) (Nov 2022) - - [Ignore Previous Prompt: Attack Techniques For Language Models](https://arxiv.org/abs/2211.09527) (Nov 2022) - - [Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods](https://arxiv.org/abs/2210.07321) (Nov 2022) - - [Teaching Algorithmic Reasoning via In-context Learning](https://arxiv.org/abs/2211.09066) (Nov 2022) - - [Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference](https://arxiv.org/abs/2211.11875) (Nov 2022) - - [Ask Me Anything: A simple strategy for prompting language models](https://paperswithcode.com/paper/ask-me-anything-a-simple-strategy-for) (Oct 2022) - - [Recitation-Augmented Language Models](https://arxiv.org/abs/2210.01296) (Oct 2022) - - [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) (Oct 2022) - - [Prompting GPT-3 To Be Reliable](https://arxiv.org/abs/2210.09150) (Oct 2022) - - [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://arxiv.org/abs/2210.02406) (Oct 2022) - - [Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought](https://arxiv.org/abs/2210.01240v3) (Oct 2022) - - [Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples](https://arxiv.org/abs/2209.02128) (Sep 2022) - - [Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning](https://arxiv.org/abs/2209.14610) (Sep 2022) - - [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://arxiv.org/abs/2209.11755) (Sep 2022) - - [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://arxiv.org/abs/2208.03299) (Nov 2022) - - [DocPrompting: Generating Code by Retrieving the Docs](https://arxiv.org/abs/2207.05987) (July 2022) - - [On the Advance of Making Language Models Better Reasoners](https://arxiv.org/abs/2206.02336) (June 2022) - - [Large Language Models are Zero-Shot Reasoners](https://arxiv.org/abs/2205.11916) (May 2022) - - [Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations](https://arxiv.org/abs/2205.11822) (May 2022) - - [MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning](https://arxiv.org/abs/2205.00445) (May 2022) + - [CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society](https://arxiv.org/abs/2303.17760) (Marzo 2023) + - [Self-Refine: Iterative Refinement with Self-Feedback](https://arxiv.org/abs/2303.17651v1) (Marzo 2023) + - [kNN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference](https://arxiv.org/abs/2303.13824) (Marzo 2023) + - [Visual-Language Prompt Tuning with Knowledge-guided Context Optimization](https://arxiv.org/abs/2303.13283) (Marzo 2023) + - [Fairness-guided Few-shot Prompting for Large Language Models](https://arxiv.org/abs/2303.13217) (Marzo 2023) + - [Context-faithful Prompting for Large Language Models](https://arxiv.org/abs/2303.11315) (Marzo 2023) + - [Is Prompt All You Need? No. A Comprehensive and Broader View of Instruction Learning](https://arxiv.org/abs/2303.10475) (Marzo 2023) + - [UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation](https://arxiv.org/abs/2303.08518) (Marzo 2023) + - [Model-tuning Via Prompts Makes NLP Models Adversarially Robust](https://arxiv.org/abs/2303.07320) (Marzo 2023) + - [Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer](https://arxiv.org/abs/2303.03922) (Marzo 2023) + - [CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification](https://arxiv.org/abs/2303.03628) (Marzo 2023) + - [Larger language models do in-context learning differently](https://arxiv.org/abs/2303.03846) (Marzo 2023) + - [OpenICL: An Open-Source Framework for In-context Learning](https://arxiv.org/abs/2303.02913) (Marzo 2023) + - [Dynamic Prompting: A Unified Framework for Prompt Tuning](https://arxiv.org/abs/2303.02909) (Marzo 2023) + - [Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning](https://arxiv.org/abs/2303.02861) (Marzo 2023) + - [Effectiveness of Data Augmentation for Prefix Tuning with Limited Data](https://arxiv.org/abs/2303.02577) (Marzo 2023) + - [Mixture of Soft Prompts for Controllable Data Generation](https://arxiv.org/abs/2303.01580) (Marzo 2023) + - [Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners](https://arxiv.org/abs/2303.02151) (Marzo 2023) + - [How Robust is GPT-3.5 to PreDicembreessors? A Comprehensive Study on Language Understanding Tasks](https://arxiv.org/abs/2303.00293) (Marzo 2023) + - [Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT](https://arxiv.org/pdf/2302.10198.pdf) (Febbraio 2023) + - [EvoPrompting: Language Models for Code-Level Neural Architecture Search](https://arxiv.org/abs/2302.14838) (Febbraio 2023) + - [In-Context Instruction Learning](https://arxiv.org/abs/2302.14691) (Febbraio 2023) + - [Chain of Hindsight Aligns Language Models with Feedback](https://arxiv.org/abs/2302.02676) (Febbraio 2023) + - [Language Is Not All You Need: Aligning Perception with Language Models](https://arxiv.org/abs/2302.14045) (Febbraio 2023) + - [Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data](https://arxiv.org/abs/2302.12822) (Febbraio 2023) + - [Active Prompting with Chain-of-Thought for Large Language Models](https://arxiv.org/abs/2302.12246) (Febbraio 2023) + - [More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models](https://arxiv.org/abs/2302.12173) (Febbraio 2023) + - [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://arxiv.org/abs/2302.11382) (Febbraio 2023) + - [Guiding Large Language Models via Directional Stimulus Prompting](https://arxiv.org/abs/2302.11520) (Febbraio 2023) + - [How Does In-Context Learning Help Prompt Tuning?](https://arxiv.org/abs/2302.11521) (Febbraio 2023) + - [Scalable Prompt Generation for Semi-supervised Learning with Language Models](https://arxiv.org/abs/2302.09236) (Febbraio 2023) + - [Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints](https://arxiv.org/abs/2302.09185) (Febbraio 2023) + - [À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting](https://arxiv.org/abs/2302.07994) (Febbraio 2023) + - [GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks](https://arxiv.org/abs/2302.08043) (Febbraio 2023) + - [The Capacity for Moral Self-Correction in Large Language Models](https://arxiv.org/abs/2302.07459) (Febbraio 2023) + - [SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains](https://arxiv.org/abs/2302.06868) (Febbraio 2023) + - [Evaluating the Robustness of Discrete Prompts](https://arxiv.org/abs/2302.05619) (Febbraio 2023) + - [Compositional Exemplars for In-context Learning](https://arxiv.org/abs/2302.05698) (Febbraio 2023) + - [Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery](https://arxiv.org/abs/2302.03668) (Febbraio 2023) + - [Multimodal Chain-of-Thought Reasoning in Language Models](https://arxiv.org/abs/2302.00923) (Febbraio 2023) + - [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://arxiv.org/abs/2302.00093) (Febbraio 2023) + - [Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models](https://arxiv.org/abs/2302.00618) (Febbraio 2023) + - [Progressive Prompts: Continual Learning for Language Models](https://arxiv.org/abs/2301.12314) (Gennaio 2023) + - [Batch Prompting: Efficient Inference with LLM APIs](https://arxiv.org/abs/2301.08721) (Gennaio 2023) + - [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP](https://arxiv.org/abs/2212.14024) (Dicembre 2022) + - [On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://arxiv.org/abs/2212.08061) (Dicembre 2022) + - [Constitutional AI: Harmlessness from AI Feedback](https://arxiv.org/abs/2212.08073) (Dicembre 2022) + - [Successive Prompting for Dicembreomposing Complex Questions](https://arxiv.org/abs/2212.04092) (Dicembre 2022) + - [Large Language Models are reasoners with Self-Verification](https://arxiv.org/abs/2212.09561v1) (Dicembre 2022) + - [Discovering Language Model Behaviors with Model-Written Evaluations](https://arxiv.org/abs/2212.09251) (Dicembre 2022) + - [Structured Prompting: Scaling In-Context Learning to 1,000 Examples](https://arxiv.org/abs/2212.06713) (Dicembre 2022) + - [PAL: Program-aided Language Models](https://arxiv.org/abs/2211.10435) (Novembre 2022) + - [Large Language Models Are Human-Level Prompt Engineers](https://arxiv.org/abs/2211.01910) (Novembre 2022) + - [Ignore Previous Prompt: Attack Techniques For Language Models](https://arxiv.org/abs/2211.09527) (Novembre 2022) + - [Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods](https://arxiv.org/abs/2210.07321) (Novembre 2022) + - [Teaching Algorithmic Reasoning via In-context Learning](https://arxiv.org/abs/2211.09066) (Novembre 2022) + - [Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference](https://arxiv.org/abs/2211.11875) (Novembre 2022) + - [Ask Me Anything: A simple strategy for prompting language models](https://paperswithcode.com/paper/ask-me-anything-a-simple-strategy-for) (Ottobre 2022) + - [Recitation-Augmented Language Models](https://arxiv.org/abs/2210.01296) (Ottobre 2022) + - [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) (Ottobre 2022) + - [Prompting GPT-3 To Be Reliable](https://arxiv.org/abs/2210.09150) (Ottobre 2022) + - [Dicembreomposed Prompting: A Modular Approach for Solving Complex Tasks](https://arxiv.org/abs/2210.02406) (Ottobre 2022) + - [Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought](https://arxiv.org/abs/2210.01240v3) (Ottobre 2022) + - [Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples](https://arxiv.org/abs/2209.02128) (Settembre 2022) + - [Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning](https://arxiv.org/abs/2209.14610) (Settembre 2022) + - [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://arxiv.org/abs/2209.11755) (Settembre 2022) + - [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://arxiv.org/abs/2208.03299) (Novembre 2022) + - [DocPrompting: Generating Code by Retrieving the Docs](https://arxiv.org/abs/2207.05987) (Luglio 2022) + - [On the Advance of Making Language Models Better Reasoners](https://arxiv.org/abs/2206.02336) (Giugnoe 2022) + - [Large Language Models are Zero-Shot Reasoners](https://arxiv.org/abs/2205.11916) (Maggio 2022) + - [Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations](https://arxiv.org/abs/2205.11822) (Maggio 2022) + - [MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning](https://arxiv.org/abs/2205.00445) (Maggio 2022) - [PPT: Pre-trained Prompt Tuning for Few-shot Learning](https://aclanthology.org/2022.acl-long.576/) (Mqy 2022) - - [Toxicity Detection with Generative Prompt-based Inference](https://arxiv.org/abs/2205.12390) (May 2022) - - [Learning to Transfer Prompts for Text Generation](https://arxiv.org/abs/2205.01543) (May 2022) - - [The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning](https://arxiv.org/abs/2205.03401) (May 2022) - - [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) (Apr 2022) - - [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://arxiv.org/abs/2203.06566) (Mar 2022) - - [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://arxiv.org/abs/2203.11171) (March 2022) + - [Toxicity Detection with Generative Prompt-based Inference](https://arxiv.org/abs/2205.12390) (Maggio 2022) + - [Learning to Transfer Prompts for Text Generation](https://arxiv.org/abs/2205.01543) (Maggio 2022) + - [The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning](https://arxiv.org/abs/2205.03401) (Maggio 2022) + - [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://arxiv.org/abs/2204.13988) (Aprile 2022) + - [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://arxiv.org/abs/2203.06566) (Marzo 2022) + - [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://arxiv.org/abs/2203.11171) (Marzo 2022) - [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155) - - [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://arxiv.org/abs/2202.12837) (Feb 2022) - - [Chain of Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) (Jan 2022) - - [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://arxiv.org/abs/2112.00114) (Nov 2021) - - [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://arxiv.org/abs/2110.01691) (Oct 2021) - - [Generated Knowledge Prompting for Commonsense Reasoning](https://arxiv.org/abs/2110.08387) (Oct 2021) - - [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) (Oct 2021) - - [Reframing Instructional Prompts to GPTk's Language](https://arxiv.org/abs/2109.07830) (Sep 2021) - - [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://arxiv.org/abs/2109.06977) (Sep 2021) - - [Making Pre-trained Language Models Better Few-shot Learners](https://aclanthology.org/2021.acl-long.295) (Aug 2021) - - [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://arxiv.org/abs/2104.08786) (April 2021) - - [BERTese: Learning to Speak to BERT](https://aclanthology.org/2021.eacl-main.316) (April 2021) - - [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/abs/2104.08691) (April 2021) - - [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://arxiv.org/abs/2102.07350) (Feb 2021) - - [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://arxiv.org/abs/2102.09690) (Feb 2021) - - [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://arxiv.org/abs/2101.00190) (Jan 2021) - - [Learning to Generate Task-Specific Adapters from Task Description](https://arxiv.org/abs/2101.00420) (Jan 2021) - - [Making Pre-trained Language Models Better Few-shot Learners](https://arxiv.org/abs/2012.15723) (Dec 2020) - - [Learning from Task Descriptions](https://aclanthology.org/2020.emnlp-main.105/) (Nov 2020) - - [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://arxiv.org/abs/2010.15980) (Oct 2020) - - [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) (May 2020) - - [How Can We Know What Language Models Know?](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00324/96460/How-Can-We-Know-What-Language-Models-Know) (July 2020) - - [Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361) (Jan 2020) + - [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://arxiv.org/abs/2202.12837) (Febbraio 2022) + - [Chain of Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/abs/2201.11903) (Gennaio 2022) + - [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://arxiv.org/abs/2112.00114) (Novembre 2021) + - [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://arxiv.org/abs/2110.01691) (Ottobre 2021) + - [Generated Knowledge Prompting for Commonsense Reasoning](https://arxiv.org/abs/2110.08387) (Ottobre 2021) + - [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) (Ottobre 2021) + - [Reframing Instructional Prompts to GPTk's Language](https://arxiv.org/abs/2109.07830) (Settembre 2021) + - [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://arxiv.org/abs/2109.06977) (Settembre 2021) + - [Making Pre-trained Language Models Better Few-shot Learners](https://aclanthology.org/2021.acl-long.295) (Agosto 2021) + - [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://arxiv.org/abs/2104.08786) (Aprile 2021) + - [BERTese: Learning to Speak to BERT](https://aclanthology.org/2021.eacl-main.316) (Aprile 2021) + - [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/abs/2104.08691) (Aprile 2021) + - [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://arxiv.org/abs/2102.07350) (Febbraio 2021) + - [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://arxiv.org/abs/2102.09690) (Febbraio 2021) + - [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://arxiv.org/abs/2101.00190) (Gennaio 2021) + - [Learning to Generate Task-Specific Adapters from Task Description](https://arxiv.org/abs/2101.00420) (Gennaio 2021) + - [Making Pre-trained Language Models Better Few-shot Learners](https://arxiv.org/abs/2012.15723) (Dicembre 2020) + - [Learning from Task Descriptions](https://aclanthology.org/2020.emnlp-main.105/) (Novembre 2020) + - [AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts](https://arxiv.org/abs/2010.15980) (Ottobre 2020) + - [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) (Maggio 2020) + - [How Can We Know What Language Models Know?](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00324/96460/How-Can-We-Know-What-Language-Models-Know) (Luglio 2020) + - [Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361) (Gennaio 2020) ## Applicazioni - - [Assessing Language Model Deployment with Risk Cards]() (April 2023) - - [Enhancing Large Language Models with Climate Resources](https://arxiv.org/abs/2304.00116) (March 2023) - - [BloombergGPT: A Large Language Model for Finance](https://arxiv.org/abs/2303.17564) (March 2023) - - [Medical Intervention Duration Estimation Using Language-enhanced Transformer Encoder with Medical Prompts](https://arxiv.org/abs/2303.17408) (March 2023) - - [Soft-prompt tuning to predict lung cancer using primary care free-text Dutch medical notes](https://arxiv.org/abs/2303.15846) (March 2023) - - [TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs](https://arxiv.org/abs/2303.16434) (March 2023) - - [Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning](https://arxiv.org/abs/2303.16445) (March 2023) - - [Linguistically Informed ChatGPT Prompts to Enhance Japanese-Chinese Machine Translation: A Case Study on Attributive Clauses](https://arxiv.org/abs/2303.15587) (March 2023) - - [Knowledge-augmented Frame Semantic Parsing with Hybrid Prompt-tuning](https://arxiv.org/abs/2303.14375) (March 2023) - - [Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation](https://arxiv.org/abs/2303.15413) (March 2023) - - [Zero-shot Model Diagnosis](https://arxiv.org/abs/2303.15441#) (March 2023) - - [Prompting Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages](https://arxiv.org/abs/2303.13592) (March 2023) - - [SPeC: A Soft Prompt-Based Calibration on Mitigating Performance Variability in Clinical Notes Summarization](https://arxiv.org/abs/2303.13035) (March 2023) - - [Large Language Models and Simple, Stupid Bugs](https://arxiv.org/abs/2303.11455) (March 2023) - - [Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses?](https://arxiv.org/abs/2303.09325) (Mar 2023) - - [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://arxiv.org/abs/2303.08896) (Mar 2023) - - [Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification](https://arxiv.org/abs/2303.07142) (March 2023) - - [ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction](https://arxiv.org/abs/2303.05063) (March 2023) - - [MathPrompter: Mathematical Reasoning using Large Language Models](https://arxiv.org/abs/2303.05398) (March 2023) - - [Prompt-Based Learning for Thread Structure Prediction in Cybersecurity Forums](https://arxiv.org/abs/2303.05400) (March 2023) - - [Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting](https://arxiv.org/abs/2303.03199) (March 2023) - - [Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering](https://arxiv.org/abs/2303.01903) (March 2023) - - [Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis](https://arxiv.org/abs/2303.00815) (March 2023) - - [SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks](https://arxiv.org/abs/2303.00733) (March 2023) - - [Goal Driven Discovery of Distributional Differences via Language Descriptions](https://arxiv.org/abs/2302.14233) (Feb 2023) - - [Navigating the Grey Area: Expressions of Overconfidence and Uncertainty in Language Models](https://arxiv.org/abs/2302.13439) (Feb 2023) - - [TabGenie: A Toolkit for Table-to-Text Generation](https://arxiv.org/abs/2302.14169) (Feb 2023) - - [SGL-PT: A Strong Graph Learner with Graph Prompt Tuning](https://arxiv.org/abs/2302.12449) (Feb 2023) - - [Few-Shot Table-to-Text Generation with Prompt-based Adapter](https://arxiv.org/abs/2302.12468) (Feb 2023) - - [Language Models Are Few-shot Learners for Prognostic Prediction](https://arxiv.org/abs/2302.12692) (Feb 2023) - - [STA: Self-controlled Text Augmentation for Improving Text Classifications](https://arxiv.org/abs/2302.12784) (Feb 2023) - - [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://arxiv.org/abs/2302.12813) (Feb 2023) - - [How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study](https://arxiv.org/abs/2302.10916) (Feb 2023) - - [Grimm in Wonderland: Prompt Engineering with Midjourney to Illustrate Fairytales](https://arxiv.org/abs/2302.08961) (Feb 2023) - - [LabelPrompt: Effective Prompt-based Learning for Relation Classification](https://arxiv.org/abs/2302.08068) (Feb 2023) - - [Language Model Crossover: Variation through Few-Shot Prompting](https://arxiv.org/abs/2302.09236) (Feb 2023) - - [Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition](https://arxiv.org/abs/2302.08102) (Feb 2023) - - [The Capacity for Moral Self-Correction in Large Language Models](https://arxiv.org/abs/2302.07459) (Feb 2023) - - [Prompting for Multimodal Hateful Meme Classification](https://arxiv.org/abs/2302.04156) (Feb 2023) - - [PLACES: Prompting Language Models for Social Conversation Synthesis](https://arxiv.org/abs/2302.03269) (Feb 2023) - - [Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation](https://arxiv.org/abs/2302.01441) (Feb 2023) - - [Crawling the Internal Knowledge-Base of Language Models](https://arxiv.org/abs/2301.12810) (Jan 2023) - - [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://arxiv.org/abs/2212.02199) (Dec 2022) - - [Investigating Prompt Engineering in Diffusion Models](https://arxiv.org/abs/2211.15462) (Nov 2022) - - [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://arxiv.org/abs/2209.09513v2) (Sep 2022) - - [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://arxiv.org/abs/2210.15157) (Oct 2022) - - [Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic?](https://arxiv.org/abs/2210.14699) (Oct 2022) - - [Plot Writing From Scratch Pre-Trained Language Models](https://aclanthology.org/2022.inlg-main.5) (July 2022) - - [Survey of Hallucination in Natural Language Generation](https://arxiv.org/abs/2202.03629) (Feb 2022) + - [Assessing Language Model Deployment with Risk Cards]() (Aprile 2023) + - [Enhancing Large Language Models with Climate Resources](https://arxiv.org/abs/2304.00116) (Marzo 2023) + - [BloombergGPT: A Large Language Model for Finance](https://arxiv.org/abs/2303.17564) (Marzo 2023) + - [Medical Intervention Duration Estimation Using Language-enhanced Transformer Encoder with Medical Prompts](https://arxiv.org/abs/2303.17408) (Marzo 2023) + - [Soft-prompt tuning to predict lung cancer using priMarzoy care free-text Dutch medical notes](https://arxiv.org/abs/2303.15846) (Marzo 2023) + - [TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs](https://arxiv.org/abs/2303.16434) (Marzo 2023) + - [Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning](https://arxiv.org/abs/2303.16445) (Marzo 2023) + - [Linguistically Informed ChatGPT Prompts to Enhance Japanese-Chinese Machine Translation: A Case Study on Attributive Clauses](https://arxiv.org/abs/2303.15587) (Marzo 2023) + - [Knowledge-augmented Frame Semantic Parsing with Hybrid Prompt-tuning](https://arxiv.org/abs/2303.14375) (Marzo 2023) + - [Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation](https://arxiv.org/abs/2303.15413) (Marzo 2023) + - [Zero-shot Model Diagnosis](https://arxiv.org/abs/2303.15441#) (Marzo 2023) + - [Prompting Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages](https://arxiv.org/abs/2303.13592) (Marzo 2023) + - [SPeC: A Soft Prompt-Based Calibration on Mitigating Performance Variability in Clinical Notes SumMarzoization](https://arxiv.org/abs/2303.13035) (Marzo 2023) + - [Large Language Models and Simple, Stupid Bugs](https://arxiv.org/abs/2303.11455) (Marzo 2023) + - [Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses?](https://arxiv.org/abs/2303.09325) (Marzo 2023) + - [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://arxiv.org/abs/2303.08896) (Marzo 2023) + - [Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification](https://arxiv.org/abs/2303.07142) (Marzo 2023) + - [ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction](https://arxiv.org/abs/2303.05063) (Marzo 2023) + - [MathPrompter: Mathematical Reasoning using Large Language Models](https://arxiv.org/abs/2303.05398) (Marzo 2023) + - [Prompt-Based Learning for Thread Structure Prediction in Cybersecurity Forums](https://arxiv.org/abs/2303.05400) (Marzo 2023) + - [Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting](https://arxiv.org/abs/2303.03199) (Marzo 2023) + - [Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering](https://arxiv.org/abs/2303.01903) (Marzo 2023) + - [Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis](https://arxiv.org/abs/2303.00815) (Marzo 2023) + - [SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks](https://arxiv.org/abs/2303.00733) (Marzo 2023) + - [Goal Driven Discovery of Distributional Differences via Language Descriptions](https://arxiv.org/abs/2302.14233) (Febbraio 2023) + - [Navigating the Grey Area: Expressions of Overconfidence and Uncertainty in Language Models](https://arxiv.org/abs/2302.13439) (Febbraio 2023) + - [TabGenie: A Toolkit for Table-to-Text Generation](https://arxiv.org/abs/2302.14169) (Febbraio 2023) + - [SGL-PT: A Strong Graph Learner with Graph Prompt Tuning](https://arxiv.org/abs/2302.12449) (Febbraio 2023) + - [Few-Shot Table-to-Text Generation with Prompt-based Adapter](https://arxiv.org/abs/2302.12468) (Febbraio 2023) + - [Language Models Are Few-shot Learners for Prognostic Prediction](https://arxiv.org/abs/2302.12692) (Febbraio 2023) + - [STA: Self-controlled Text Augmentation for Improving Text Classifications](https://arxiv.org/abs/2302.12784) (Febbraio 2023) + - [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://arxiv.org/abs/2302.12813) (Febbraio 2023) + - [How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study](https://arxiv.org/abs/2302.10916) (Febbraio 2023) + - [Grimm in Wonderland: Prompt Engineering with Midjourney to Illustrate Fairytales](https://arxiv.org/abs/2302.08961) (Febbraio 2023) + - [LabelPrompt: Effective Prompt-based Learning for Relation Classification](https://arxiv.org/abs/2302.08068) (Febbraio 2023) + - [Language Model Crossover: Variation through Few-Shot Prompting](https://arxiv.org/abs/2302.09236) (Febbraio 2023) + - [Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition](https://arxiv.org/abs/2302.08102) (Febbraio 2023) + - [The Capacity for Moral Self-Correction in Large Language Models](https://arxiv.org/abs/2302.07459) (Febbraio 2023) + - [Prompting for Multimodal Hateful Meme Classification](https://arxiv.org/abs/2302.04156) (Febbraio 2023) + - [PLACES: Prompting Language Models for Social Conversation Synthesis](https://arxiv.org/abs/2302.03269) (Febbraio 2023) + - [Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation](https://arxiv.org/abs/2302.01441) (Febbraio 2023) + - [Crawling the Internal Knowledge-Base of Language Models](https://arxiv.org/abs/2301.12810) (Gennaio 2023) + - [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://arxiv.org/abs/2212.02199) (Dicembre 2022) + - [Investigating Prompt Engineering in Diffusion Models](https://arxiv.org/abs/2211.15462) (Novembre 2022) + - [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://arxiv.org/abs/2209.09513v2) (Settembre 2022) + - [Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language](https://arxiv.org/abs/2210.15157) (Ottobre 2022) + - [Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic?](https://arxiv.org/abs/2210.14699) (Ottobre 2022) + - [Plot Writing From Scratch Pre-Trained Language Models](https://aclanthology.org/2022.inlg-main.5) (Luglio 2022) + - [Survey of Hallucination in Natural Language Generation](https://arxiv.org/abs/2202.03629) (Febbraio 2022) ## Collezioni diff --git a/pages/techniques/ape.it.mdx b/pages/techniques/ape.it.mdx index 14fa22d..97b0e06 100644 --- a/pages/techniques/ape.it.mdx +++ b/pages/techniques/ape.it.mdx @@ -6,7 +6,7 @@ import APE from '../../img/APE.png' import APECOT from '../../img/ape-zero-shot-cot.png' -Image Source: [Zhou et al., (2022)](https://arxiv.org/abs/2211.01910) +sorgente immagine: [Zhou et al., (2022)](https://arxiv.org/abs/2211.01910) [Zhou et al., (2022)](https://arxiv.org/abs/2211.01910) propone automatic prompt engineer (APE), un framework per la generazione e la selezione automatica delle istruzioni. Il problema della generazione delle istruzioni viene inquadrato come sintesi del linguaggio naturale e affrontato come un problema di ottimizzazione black-box che utilizza gli LLM per generare e ricercare le soluzioni candidate. @@ -17,7 +17,7 @@ APE scopre un prompt di zero-shot CoT migliore del prompt "Pensiamo passo dopo p Il prompt "Lavoriamo passo dopo passo per essere sicuri di avere la risposta giusta" suscita un ragionamento a catena e migliora le prestazioni nei benchmark MultiArith e GSM8K: -Image Source: [Zhou et al., (2022)](https://arxiv.org/abs/2211.01910) +sorgente immagine: [Zhou et al., (2022)](https://arxiv.org/abs/2211.01910) Questa ricerca tratta un argomento importante legato al prompt engineering, ovvero l'idea di ottimizzare automaticamente i prompt. Anche se in questa guida non approfondiamo l'argomento, ecco alcuni documenti chiave se siete interessati all'argomento: diff --git a/pages/techniques/cot.it.mdx b/pages/techniques/cot.it.mdx index 463728d..66cd1d1 100644 --- a/pages/techniques/cot.it.mdx +++ b/pages/techniques/cot.it.mdx @@ -9,7 +9,7 @@ import ZEROCOT from '../../img/zero-cot.png' -Image Source: [Wei et al. (2022)](https://arxiv.org/abs/2201.11903) +sorgente immagine: [Wei et al. (2022)](https://arxiv.org/abs/2201.11903) Introdotto in [Wei et al. (2022)](https://arxiv.org/abs/2201.11903), il prompt a catena di pensieri (CoT) consente di ottenere capacità di ragionamento complesse attraverso fasi di ragionamento intermedie. Si può combinare con il prompt few-shot per ottenere risultati migliori su compiti più complessi che richiedono un ragionamento prima di rispondere. @@ -58,7 +58,7 @@ Si tenga presente che gli autori sostengono che si tratta di una capacità emerg -Image Source: [Kojima et al. (2022)](https://arxiv.org/abs/2205.11916) +sorgente immagine: [Kojima et al. (2022)](https://arxiv.org/abs/2205.11916) Un'idea emersa più di recente è quella della [zero-shot CoT](https://arxiv.org/abs/2205.11916) (Kojima et al. 2022) che consiste essenzialmente nell'aggiungere "Pensiamo passo dopo passo" al prompt originale. Proviamo un problema semplice e vediamo come si comporta il modello: diff --git a/pages/techniques/dsp.it.mdx b/pages/techniques/dsp.it.mdx index 77ef8ea..fbb4033 100644 --- a/pages/techniques/dsp.it.mdx +++ b/pages/techniques/dsp.it.mdx @@ -11,6 +11,6 @@ Per generare lo stimolo/il suggerimento viene addestrata una politica LM sintoni La figura seguente mostra come il Directional Stimulus Prompting si confronta con il prompt standard. La politica LM può essere piccola e ottimizzata per generare i suggerimenti che guidano un LLM congelato black-box. -Image Source: [Li et al., (2023)](https://arxiv.org/abs/2302.11520) +Sorgente immagine: [Li et al., (2023)](https://arxiv.org/abs/2302.11520) Esempio completo in arrivo!