@ -145,12 +145,34 @@ The current recommendation for `gpt-3.5-turbo-0301` is to add instructions in th
---
## References
- [Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study](https://arxiv.org/abs/2304.04339) (April 2023)
- [A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding](https://arxiv.org/abs/2304.04256) (April 2023)
- [Extractive Summarization via ChatGPT for Faithful Summary Generation](https://arxiv.org/abs/2304.04193) (April 2023)
- [What does ChatGPT return about human values? Exploring value bias in ChatGPT using a descriptive value theory](https://arxiv.org/abs/2304.03612) (April 2023)
- [On the Evaluations of ChatGPT and Emotion-enhanced Prompting for Mental Health Analysis](https://arxiv.org/abs/2304.03347) (April 2023)
- [ChatGPT-Crawler: Find out if ChatGPT really knows what it's talking about](https://arxiv.org/abs/2304.03325) (April 2023)
- [Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models](https://arxiv.org/abs/2304.03738) (April 2023)
- [Synthesis of Mathematical programs from Natural Language Specifications](https://arxiv.org/abs/2304.03287) (April 2023)
- [Large language models effectively leverage document-level context for literary translation, but critical errors persist](https://arxiv.org/abs/2304.03245) (April 2023)
- [Investigating Chain-of-thought with ChatGPT for Stance Detection on Social Media](https://arxiv.org/abs/2304.03087) (April 2023)
- [ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model](https://arxiv.org/abs/2304.03086) (April 2023)
- [Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions](https://arxiv.org/abs/2304.02868) (April 2023)
- [Human-like Summarization Evaluation with ChatGPT](https://arxiv.org/abs/2304.02554) (April 2023)
- [Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification](https://arxiv.org/abs/2304.02496) (April 2023)
- [Comparative Analysis of CHATGPT and the evolution of language models](https://arxiv.org/abs/2304.02468) (April 2023)
- [Unleashing the Power of ChatGPT for Translation: An Empirical Study](https://arxiv.org/abs/2304.02182) (April 2023)
- [Geotechnical Parrot Tales (GPT): Overcoming GPT hallucinations with prompt engineering for geotechnical applications](https://arxiv.org/abs/2304.02138) (April 2023)
- [Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing](https://arxiv.org/abs/2304.02017) (April 2023)
- [Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models](https://arxiv.org/abs/2304.01852) (April 2023)
- [Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation](https://arxiv.org/abs/2304.01746) (April 2023)
- [Safety Analysis in the Era of Large Language Models: A Case Study of STPA using ChatGPT](https://arxiv.org/abs/2304.01246) (April 2023)
- [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)
- [How well do Large Language Models perform in Arithmetic tasks?](https://arxiv.org/abs/2304.02015) (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)
| [PanGu-Σ](https://arxiv.org/abs/2303.10845v1) | 2023 | PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing |
| [BloombergGPT](https://arxiv.org/abs/2303.17564v1)| 2023 |BloombergGPT: A Large Language Model for Finance|
| [BloombergGPT](https://arxiv.org/abs/2303.17564v1)| 2023 |BloombergGPT: A Large Language Model for Finance|
| [Cerebras-GPT](https://arxiv.org/abs/2304.03208) | 2023 | Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
@ -121,6 +125,15 @@ The following are the latest papers (sorted by release date) on prompt engineeri
## Applications
- [Automated Reading Passage Generation with OpenAI's Large Language Model](https://arxiv.org/abs/2304.04616) (April 2023)
- [WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus](https://arxiv.org/abs/2304.04358) (April 2023)
- [Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition](https://arxiv.org/abs/2304.04704) (April 2023)
- [GPT detectors are biased against non-native English writers](https://arxiv.org/abs/2304.02819) (April 2023)
- [Zero-Shot Next-Item Recommendation using Large Pretrained Language Models](https://arxiv.org/abs/2304.03153) (April 2023)
- [Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT](https://arxiv.org/abs/2304.02213) (April 2023)
- [Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning](https://arxiv.org/abs/2304.01295) (April 2023)
- [Better Language Models of Code through Self-Improvement](https://arxiv.org/abs/2304.01228) (April 2023)
- [PromptORE -- A Novel Approach Towards Fully Unsupervised Relation Extraction](https://arxiv.org/abs/2304.01209) (April)
- [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)
- [Common Sense as Dark Matter - Yejin Choi | Stanford MLSys #78](https://youtube.com/live/n4HakBqoCVg?feature=shares)
- [Create images with your words – Bing Image Creator comes to the new Bing](https://blogs.microsoft.com/blog/2023/03/21/create-images-with-your-words-bing-image-creator-comes-to-the-new-bing/)
@ -51,12 +52,14 @@
- [If I Was Starting Prompt Engineering in 2023: My 8 Insider Tips](https://youtube.com/watch?v=SirW7feTjh0&feature=shares)
- [Indirect Prompt Injection on Bing Chat](https://greshake.github.io/)
- [Interactive guide to GPT-3 prompt parameters](https://sevazhidkov.com/interactive-guide-to-gpt-3-prompt-parameters)
- [Introduction to ChatGPT](https://www.edx.org/course/introduction-to-chatgpt)
- [Introduction to Reinforcement Learning with Human Feedback](https://www.surgehq.ai/blog/introduction-to-reinforcement-learning-with-human-feedback-rlhf-series-part-1)
- [In defense of prompt engineering](https://simonwillison.net/2023/Feb/21/in-defense-of-prompt-engineering/)
- [JailBreaking ChatGPT: Everything You Need to Know](https://metaroids.com/learn/jailbreaking-chatgpt-everything-you-need-to-know/)
- [Language Models and Prompt Engineering: Systematic Survey of Prompting Methods in NLP](https://youtube.com/watch?v=OsbUfL8w-mo&feature=shares)
- [Language Model Behavior: A Comprehensive Survey](https://arxiv.org/abs/2303.11504)
@ -95,4 +95,4 @@ While that last sentence is somewhat subjective, I flipped the distribution and
### Order of Exemplars
When performing few-shot learning, does the order affect the performance of the model or bias the model in some way?
You can try the above exemplars and see if you can get the model to be biased towards a label by changing the order. The advice is to randomly order exemplars. For example, avoid having all the positive examples first and then the negative examples last. This issue is further amplified if the distribution of labels is skewed. Always ensure to experiment a lot to reduce this type of bias.
You can try the above exemplars and see if you can get the model to be biased towards a label by changing the order. The advice is to randomly order exemplars. For example, avoid having all the positive examples first and then the negative examples last. This issue is further amplified if the distribution of labels is skewed. Always ensure to experiment a lot to reduce this type of bias.
Elvis Saravia
1 year ago