From edb3e91c4cc79dc6bfdf6b9c92840dd70d991318 Mon Sep 17 00:00:00 2001 From: Elvis Saravia Date: Fri, 2 Jun 2023 09:39:22 -0600 Subject: [PATCH] updated llm collection --- pages/models/_meta.en.json | 2 +- pages/models/collection.en.mdx | 135 ++++++++++++++++++--------------- pages/papers.en.mdx | 1 + 3 files changed, 77 insertions(+), 61 deletions(-) diff --git a/pages/models/_meta.en.json b/pages/models/_meta.en.json index a449f46..5f78dd4 100644 --- a/pages/models/_meta.en.json +++ b/pages/models/_meta.en.json @@ -3,6 +3,6 @@ "chatgpt": "ChatGPT", "llama": "LLaMA", "gpt-4": "GPT-4", - "collection": "Model Collection" + "collection": "LLM Collection" } \ No newline at end of file diff --git a/pages/models/collection.en.mdx b/pages/models/collection.en.mdx index d5c4095..b061a4e 100644 --- a/pages/models/collection.en.mdx +++ b/pages/models/collection.en.mdx @@ -1,67 +1,82 @@ -# Model Collection +# LLM Collection import { Callout, FileTree } from 'nextra-theme-docs' - - This section is under heavy development. - +This section consists of a collection and summary of notable and foundational LLMs. -This section consists of a collection and summary of notable and foundational LLMs. (Data adopted from [Papers with Code](https://paperswithcode.com/methods/category/language-models) and the recent work by [Zhao et al. (2023)](https://arxiv.org/pdf/2303.18223.pdf). +## Models +| Model | Release Date | Size (B) | Checkpoints | Description | +| --- | --- | --- | --- | --- | +| [Falcon LLM](https://falconllm.tii.ae/) | May 2023 | 7, 40 | [Falcon-7B](https://huggingface.co/tiiuae), [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) | Falcon LLM is a foundational large language model (LLM) with 40 billion parameters trained on one trillion tokens. TII has now released Falcon LLM – a 40B model. | +| [PaLM 2](https://arxiv.org/abs/2305.10403) | May 2023 | - | - | A Language Model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. | +| [RedPajama-INCITE](https://www.together.xyz/blog/redpajama-models-v1) | May 2023 | 3, 7 | [RedPajama-INCITE](https://huggingface.co/togethercomputer) | A family of models including base, instruction-tuned & chat models. | +| [Replit Code](https://huggingface.co/replit) | May 2023 | 3 | [Replit Code](https://huggingface.co/replit) | replit-code-v1-3b model is a 2.7B LLM trained on 20 languages from the Stack Dedup v1.2 dataset. | +| [h2oGPT](https://github.com/h2oai/h2ogpt) | May 2023 | 12 | [h2oGPT](https://github.com/h2oai/h2ogpt) | h2oGPT is a large language model (LLM) fine-tuning framework and chatbot UI with document(s) question-answer capabilities. | +| [CodeGen2](https://arxiv.org/abs/2305.02309) | May 2023 | 1, 3, 7, 16 | [CodeGen2](https://github.com/salesforce/codegen2) | Code models for program synthesis. | +| [CodeT5 and CodeT5+](https://arxiv.org/abs/2305.07922) | May 2023 | 16 | [CodeT5](https://github.com/salesforce/codet5) | CodeT5 and CodeT5+ models for Code Understanding and Generation from Salesforce Research. | +| [StarCoder](https://huggingface.co/blog/starcoder) | May 2023 | 15 | [StarCoder](https://huggingface.co/bigcode/starcoder) | StarCoder: A State-of-the-Art LLM for Code | +| [MPT-7B](https://www.mosaicml.com/blog/mpt-7b) | May 2023 | 7 | [MPT-7B](https://github.com/mosaicml/llm-foundry#mpt) | MPT-7B is a GPT-style model, and the first in the MosaicML Foundation Series of models. | +| [DLite](https://medium.com/ai-squared/announcing-dlite-v2-lightweight-open-llms-that-can-run-anywhere-a852e5978c6e) | May 2023 | 0.124 - 1.5 | [DLite-v2-1.5B](https://huggingface.co/aisquared/dlite-v2-1_5b) | Lightweight instruction following models which exhibit ChatGPT-like interactivity. | +| [Dolly](https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm) | April 2023 | 3, 7, 12 | [Dolly](https://huggingface.co/databricks/dolly-v2-12b) | An instruction-following LLM, fine-tuned on a human-generated instruction dataset licensed for research and commercial use. | +| [StableLM](https://github.com/Stability-AI/StableLM#stablelm-alpha) | April 2023 | 3, 7 | [StableLM-Alpha](https://github.com/Stability-AI/StableLM#stablelm-alpha) | Stability AI's StableLM series of language models | +| [Pythia](https://arxiv.org/abs/2304.01373) | April 2023 | 0.070 - 12 | [Pythia](https://github.com/eleutherai/pythia) | A suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. | +| [Open Assistant (Pythia Family)](https://open-assistant.io/) | March 2023 | 12 | [Open Assistant](https://huggingface.co/OpenAssistant) | OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so. | +| [Cerebras-GPT](https://arxiv.org/abs/2304.03208) | March 2023 | 0.111 - 13 | [Cerebras-GPT](https://huggingface.co/cerebras) | Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster | +| [BloombergGPT](https://arxiv.org/abs/2303.17564v1)| March 2023 | 50 | - | BloombergGPT: A Large Language Model for Finance| +| [PanGu-Σ](https://arxiv.org/abs/2303.10845v1) | March 2023 | 1085 | - | PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing | +| [GPT-4](https://arxiv.org/abs/2303.08774v3) | March 2023 | - | - | GPT-4 Technical Report | +| [LLaMA](https://arxiv.org/abs/2302.13971v1) | Feb 2023 | 7, 13, 33, 65 | [LLaMA](https://github.com/facebookresearch/llama) | LLaMA: Open and Efficient Foundation Language Models | +| [Galactica](https://arxiv.org/abs/2211.09085v1) | Nov 2022 | 0.125 - 120 | [Galactica](https://huggingface.co/models?other=galactica) | Galactica: A Large Language Model for Science | +| [mT0](https://arxiv.org/abs/2211.01786v1) | Nov 2022 | 13 | [mT0-xxl](https://huggingface.co/bigscience/mt0-xxl) | Crosslingual Generalization through Multitask Finetuning | +| [BLOOM](https://arxiv.org/abs/2211.05100v3) | Nov 2022 | 176 | [BLOOM](https://huggingface.co/bigscience/bloom) | BLOOM: A 176B-Parameter Open-Access Multilingual Language Model | +| [U-PaLM](https://arxiv.org/abs/2210.11399v2) | Oct 2022 | 540 | - | Transcending Scaling Laws with 0.1% Extra Compute | +| [UL2](https://arxiv.org/abs/2205.05131v3) | Oct 2022 | 20 | [UL2, Flan-UL2](https://github.com/google-research/google-research/tree/master/ul2#checkpoints) | UL2: Unifying Language Learning Paradigms | +| [Sparrow](https://arxiv.org/abs/2209.14375) | Sep 2022 | 70 | - | Improving alignment of dialogue agents via targeted human judgements | +| [Flan-T5](https://arxiv.org/abs/2210.11416v5) | Oct 2022 | 11 | [Flan-T5-xxl](https://huggingface.co/google/flan-t5-xxl) | Scaling Instruction-Finetuned Language Models | +| [AlexaTM](https://arxiv.org/abs/2208.01448v2) | Aug 2022 | 20 | - | AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model | +| [GLM-130B](https://arxiv.org/abs/2210.02414v1) | Oct 2022 | 130 | [GLM-130B](https://github.com/THUDM/GLM-130B) | GLM-130B: An Open Bilingual Pre-trained Model | +| [OPT-IML](https://arxiv.org/abs/2212.12017v3) | Dec 2022 | 30, 175 | [OPT-IML](https://github.com/facebookresearch/metaseq/tree/main/projects/OPT-IML#pretrained-model-weights) | OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization | +| [OPT](https://arxiv.org/abs/2205.01068) | May 2022 | 175 | [OPT-13B](https://huggingface.co/facebook/opt-13b), [OPT-66B](https://huggingface.co/facebook/opt-66b) | OPT: Open Pre-trained Transformer Language Models | +| [PaLM](https://arxiv.org/abs/2204.02311v5) |April 2022| 540 | - | PaLM: Scaling Language Modeling with Pathways | +| [Tk-Instruct](https://arxiv.org/abs/2204.07705v3) | April 2022 | 11 | [Tk-Instruct-11B](https://huggingface.co/allenai/tk-instruct-11b-def) | Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks | +| [GPT-NeoX-20B](https://arxiv.org/abs/2204.06745v1) | April 2022 | 20 | [GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b) | GPT-NeoX-20B: An Open-Source Autoregressive Language Model | +| [Chinchilla](https://arxiv.org/abs/2203.15556) | Mar 2022 | 70 | - | Shows that for a compute budget, the best performances are not achieved by the largest models but by smaller models trained on more data. | +| [InstructGPT](https://arxiv.org/abs/2203.02155v1) | Mar 2022 | 175 | - | Training language models to follow instructions with human feedback | +| [CodeGen](https://arxiv.org/abs/2203.13474v5) | Mar 2022 | 0.350 - 16 | [CodeGen](https://huggingface.co/models?search=salesforce+codegen) | CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis | +| [AlphaCode](https://arxiv.org/abs/2203.07814v1) | Feb 2022 | 41 | - | Competition-Level Code Generation with AlphaCode | +| [MT-NLG](https://arxiv.org/abs/2201.11990v3) | Jan 2022 | 530 | - | Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model| +| [LaMDA](https://arxiv.org/abs/2201.08239v3) | Jan 2022 | 137 | - | LaMDA: Language Models for Dialog Applications | +| [GLaM](https://arxiv.org/abs/2112.06905) | Dec 2021 | 1200 | - | GLaM: Efficient Scaling of Language Models with Mixture-of-Experts | +| [Gopher](https://arxiv.org/abs/2112.11446v2) | Dec 2021 | 280 | - | Scaling Language Models: Methods, Analysis & Insights from Training Gopher | +| [WebGPT](https://arxiv.org/abs/2112.09332v3) | Dec 2021 | 175 | - | WebGPT: Browser-assisted question-answering with human feedback | +| [Yuan 1.0](https://arxiv.org/abs/2110.04725v2) | Oct 2021| 245 | - | Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning | +| [T0](https://arxiv.org/abs/2110.08207) | Oct 2021 | 11 | [T0](https://huggingface.co/bigscience/T0) | Multitask Prompted Training Enables Zero-Shot Task Generalization | +| [FLAN](https://arxiv.org/abs/2109.01652v5) | Sep 2021 | 137 | - | Finetuned Language Models Are Zero-Shot Learners | +| [HyperCLOVA](https://arxiv.org/abs/2109.04650) | Sep 2021 | 82 | - | What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers | +| [ERNIE 3.0 Titan](https://arxiv.org/abs/2112.12731v1) | July 2021 | 10 | - | ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation | +| [Jurassic-1](https://uploads-ssl.webflow.com/60fd4503684b466578c0d307/61138924626a6981ee09caf6_jurassic_tech_paper.pdf) | Aug 2021 | 178 | - | Jurassic-1: Technical Details and Evaluation | +| [ERNIE 3.0](https://arxiv.org/abs/2107.02137v1) | July 2021 | 10 | - | ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation| +| [Codex](https://arxiv.org/abs/2107.03374v2) | July 2021 | 12 | - | Evaluating Large Language Models Trained on Code | +| [GPT-J-6B](https://arankomatsuzaki.wordpress.com/2021/06/04/gpt-j/) | June 2021 | 6 | [GPT-J-6B](https://github.com/kingoflolz/mesh-transformer-jax/#gpt-j-6b) | A 6 billion parameter, autoregressive text generation model trained on The Pile. | +| [CPM-2](https://arxiv.org/abs/2106.10715v3) | Jun 2021 | 198 | [CPM](https://github.com/TsinghuaAI/CPM) | CPM-2: Large-scale Cost-effective Pre-trained Language Models | +| [PanGu-α](https://arxiv.org/abs/2104.12369v1) | April 2021 | 13 | [PanGu-α](https://gitee.com/mindspore/models/tree/master/official/nlp/Pangu_alpha#download-the-checkpoint) | PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation | +| [mT5](https://arxiv.org/abs/2010.11934v3) | Oct 2020 | 13 | [mT5](https://github.com/google-research/multilingual-t5#released-model-checkpoints) | mT5: A massively multilingual pre-trained text-to-text transformer | +| [BART](https://arxiv.org/abs/1910.13461) | Jul 2020 | - | [BART](https://github.com/facebookresearch/fairseq) | Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension | +| [GShard](https://arxiv.org/abs/2006.16668v1) | Jun 2020 | 600| -| GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding | +| [GPT-3](https://arxiv.org/abs/2005.14165) | May 2020 | 175 | - | Language Models are Few-Shot Learners | +| [CTRL](https://arxiv.org/abs/1909.05858) | Sep 2019 | 1.63 | [CTRL](https://github.com/salesforce/ctrl) | CTRL: A Conditional Transformer Language Model for Controllable Generation | +| [ALBERT](https://arxiv.org/abs/1909.11942) | Sep 2019 | 0.235 | [ALBERT](https://github.com/google-research/ALBERT) | A Lite BERT for Self-supervised Learning of Language Representations | +| [XLNet](https://arxiv.org/abs/1906.08237) | Jun 2019 | - | [XLNet](https://github.com/zihangdai/xlnet#released-models) | Generalized Autoregressive Pretraining for Language Understanding and Generation | +| [T5](https://arxiv.org/abs/1910.10683) | Oct 2019 | 0.06 - 11 | [Flan-T5](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints) | Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer | +| [GPT-2](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) | Nov 2019 | 1.5 | [GPT-2](https://github.com/openai/gpt-2) | Language Models are Unsupervised Multitask Learners | +| [RoBERTa](https://arxiv.org/abs/1907.11692) | July 2019 | 0.125 - 0.355 | [RoBERTa](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta) | A Robustly Optimized BERT Pretraining Approach | +| [BERT](https://arxiv.org/abs/1810.04805)| Oct 2018 | - | [BERT](https://github.com/google-research/bert) | Bidirectional Encoder Representations from Transformers | +| [GPT](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf) | June 2018 | - | [GPT](https://github.com/openai/finetune-transformer-lm) | Improving Language Understanding by Generative Pre-Training | -## Models -| Model | Release Date | Description | -| --- | --- | --- | -| [BERT](https://arxiv.org/abs/1810.04805)| 2018 | Bidirectional Encoder Representations from Transformers | -| [GPT](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf) | 2018 | Improving Language Understanding by Generative Pre-Training | -| [RoBERTa](https://arxiv.org/abs/1907.11692) | 2019 | A Robustly Optimized BERT Pretraining Approach | -| [GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) | 2019 | Language Models are Unsupervised Multitask Learners | -| [T5](https://arxiv.org/abs/1910.10683) | 2019 | Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer | -| [BART](https://arxiv.org/abs/1910.13461) | 2019 | Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension | -| [ALBERT](https://arxiv.org/abs/1909.11942) |2019 | A Lite BERT for Self-supervised Learning of Language Representations | -| [XLNet](https://arxiv.org/abs/1906.08237) | 2019 | Generalized Autoregressive Pretraining for Language Understanding and Generation | -| [CTRL](https://arxiv.org/abs/1909.05858) |2019 | CTRL: A Conditional Transformer Language Model for Controllable Generation | -| [ERNIE](https://arxiv.org/abs/1904.09223v1) | 2019| ERNIE: Enhanced Representation through Knowledge Integration | -| [GShard](https://arxiv.org/abs/2006.16668v1) | 2020 | GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding | -| [GPT-3](https://arxiv.org/abs/2005.14165) | 2020 | Language Models are Few-Shot Learners | -| [LaMDA](https://arxiv.org/abs/2201.08239v3) | 2021 | LaMDA: Language Models for Dialog Applications | -| [PanGu-α](https://arxiv.org/abs/2104.12369v1) | 2021 | PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation | -| [mT5](https://arxiv.org/abs/2010.11934v3) | 2021 | mT5: A massively multilingual pre-trained text-to-text transformer | -| [CPM-2](https://arxiv.org/abs/2106.10715v3) | 2021 | CPM-2: Large-scale Cost-effective Pre-trained Language Models | -| [T0](https://arxiv.org/abs/2110.08207) |2021 |Multitask Prompted Training Enables Zero-Shot Task Generalization | -| [HyperCLOVA](https://arxiv.org/abs/2109.04650) | 2021 | What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers | -| [Codex](https://arxiv.org/abs/2107.03374v2) |2021 |Evaluating Large Language Models Trained on Code | -| [ERNIE 3.0](https://arxiv.org/abs/2107.02137v1) | 2021 | ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation| -| [Jurassic-1](https://uploads-ssl.webflow.com/60fd4503684b466578c0d307/61138924626a6981ee09caf6_jurassic_tech_paper.pdf) | 2021 | Jurassic-1: Technical Details and Evaluation | -| [FLAN](https://arxiv.org/abs/2109.01652v5) | 2021 | Finetuned Language Models Are Zero-Shot Learners | -| [MT-NLG](https://arxiv.org/abs/2201.11990v3) | 2021 | Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model| -| [Yuan 1.0](https://arxiv.org/abs/2110.04725v2) | 2021| Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning | -| [WebGPT](https://arxiv.org/abs/2112.09332v3) | 2021 | WebGPT: Browser-assisted question-answering with human feedback | -| [Gopher](https://arxiv.org/abs/2112.11446v2) |2021 | Scaling Language Models: Methods, Analysis & Insights from Training Gopher | -| [ERNIE 3.0 Titan](https://arxiv.org/abs/2112.12731v1) |2021 | ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation | -| [GLaM](https://arxiv.org/abs/2112.06905) | 2021 | GLaM: Efficient Scaling of Language Models with Mixture-of-Experts | -| [InstructGPT](https://arxiv.org/abs/2203.02155v1) | 2022 | Training language models to follow instructions with human feedback | -| [GPT-NeoX-20B](https://arxiv.org/abs/2204.06745v1) | 2022 | GPT-NeoX-20B: An Open-Source Autoregressive Language Model | -| [AlphaCode](https://arxiv.org/abs/2203.07814v1) | 2022 | Competition-Level Code Generation with AlphaCode | -| [CodeGen](https://arxiv.org/abs/2203.13474v5) | 2022 | CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis | -| [Chinchilla](https://arxiv.org/abs/2203.15556) | 2022 | Shows that for a compute budget, the best performances are not achieved by the largest models but by smaller models trained on more data. | -| [Tk-Instruct](https://arxiv.org/abs/2204.07705v3) | 2022 | Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks | -| [UL2](https://arxiv.org/abs/2205.05131v3) | 2022 | UL2: Unifying Language Learning Paradigms | -| [PaLM](https://arxiv.org/abs/2204.02311v5) |2022| PaLM: Scaling Language Modeling with Pathways | -| [OPT](https://arxiv.org/abs/2205.01068) | 2022 | OPT: Open Pre-trained Transformer Language Models | -| [BLOOM](https://arxiv.org/abs/2211.05100v3) | 2022 | BLOOM: A 176B-Parameter Open-Access Multilingual Language Model | -| [GLM-130B](https://arxiv.org/abs/2210.02414v1) | 2022 | GLM-130B: An Open Bilingual Pre-trained Model | -| [AlexaTM](https://arxiv.org/abs/2208.01448v2) | 2022 | AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model | -| [Flan-T5](https://arxiv.org/abs/2210.11416v5) | 2022 | Scaling Instruction-Finetuned Language Models | -| [Sparrow](https://arxiv.org/abs/2209.14375) | 2022 | Improving alignment of dialogue agents via targeted human judgements | -| [U-PaLM](https://arxiv.org/abs/2210.11399v2) | 2022 | Transcending Scaling Laws with 0.1% Extra Compute | -| [mT0](https://arxiv.org/abs/2211.01786v1) | 2022 | Crosslingual Generalization through Multitask Finetuning | -| [Galactica](https://arxiv.org/abs/2211.09085v1) | 2022 | Galactica: A Large Language Model for Science | -| [OPT-IML](https://arxiv.org/abs/2212.12017v3) | 2022 | OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization | -| [LLaMA](https://arxiv.org/abs/2302.13971v1) | 2023 | LLaMA: Open and Efficient Foundation Language Models | -| [GPT-4](https://arxiv.org/abs/2303.08774v3) | 2023 |GPT-4 Technical Report | -| [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| -| [Cerebras-GPT](https://arxiv.org/abs/2304.03208) | 2023 | Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster | -| [PaLM 2](https://ai.google/static/documents/palm2techreport.pdf) | 2023 | A Language Model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. | \ No newline at end of file + + This section is under development. + + +Data adopted from [Papers with Code](https://paperswithcode.com/methods/category/language-models) and the recent work by [Zhao et al. (2023)](https://arxiv.org/pdf/2303.18223.pdf). \ No newline at end of file diff --git a/pages/papers.en.mdx b/pages/papers.en.mdx index fa085d4..e14979d 100644 --- a/pages/papers.en.mdx +++ b/pages/papers.en.mdx @@ -5,6 +5,7 @@ The following are the latest papers (sorted by release date) on prompt engineeri ## Overviews - [Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study](https://arxiv.org/abs/2305.13860) (May 2023) + - [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://arxiv.org/abs/2304.13712) (April 2023) - [Tool Learning with Foundation Models](https://arxiv.org/abs/2304.08354) (April 2023) - [One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era](https://arxiv.org/abs/2304.06488) (April 2023) - [A Bibliometric Review of Large Language Models Research from 2017 to 2023](https://arxiv.org/abs/2304.02020) (April 2023)