https://github.com/wenet-e2e/llm-papers

List of Large Lanugage Model Papers
https://github.com/wenet-e2e/llm-papers

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List of Large Lanugage Model Papers

• Host: GitHub
• URL: https://github.com/wenet-e2e/llm-papers
• Owner: wenet-e2e
• License: apache-2.0
• Created: 2023-06-02T15:36:34.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-06-05T14:59:02.000Z (over 1 year ago)
• Last Synced: 2024-08-02T12:21:21.041Z (3 months ago)
• Size: 10.7 KB
• Stars: 51
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# llm-papers

List of Large Lanugage Model Papers

## GPTs by OpenAI

- GPT-1: [Improving Language Understanding by Generative Pre-Training](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) (2018)

- GPT-2: [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) (2019)

- GPT-3: [Language Models are Few-Shot Learners](https://arxiv.org/pdf/2005.14165.pdf) (2020)

- InstructGPT: [Training language models to follow instructions with human feedback](https://arxiv.org/pdf/2203.02155.pdf) (2022)

- ChatGPT: [Introducing ChatGPT](https://openai.com/blog/chatgpt), blog (2022)

- GPT-4: [GPT-4 Technical Report](https://arxiv.org/pdf/2303.08774.pdf) (2023)

## Prompt

- Chain-of-Thought: [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/pdf/2201.11903.pdf) (Google, NeurIPS, 2022)

- ReAct: [REACT: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS](https://arxiv.org/pdf/2210.03629.pdf) (Google, ICLR, 2023)

- Self-Ask: [MEASURING AND NARROWING THE COMPOSITIONALITY GAP IN LANGUAGE MODELS](https://arxiv.org/pdf/2210.03350.pdf) (UW, 2023)

## Finetune

- Prompt Tuning: [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/pdf/2104.08691.pdf) (Google, EMNLP, 2021)

- Prefix Tuning: [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://arxiv.org/pdf/2101.00190.pdf) (Stanford, IJCNLP, 2021)

- LoRA: [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/pdf/2106.09685.pdf) (Microsoft, ICLR, 2022)

- P-Tuning: [P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks](https://aclanthology.org/2022.acl-short.8.pdf) (Tsinghua, ACL, 2022)

- P-Tuning v2: [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://arxiv.org/pdf/2110.07602.pdf) (Tsinghua, ACL, 2022)

- AdaLoRA: [Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning](https://arxiv.org/pdf/2303.10512.pdf) (Georgia Tech, ICLR, 2023)

- QLoRA: [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/pdf/2305.14314.pdf) (UW, Submitted to NeurIPS, 2023)

## Multi Modality

### Image

- BLIP-2: [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/pdf/2301.12597.pdf) (Salesforce, 2023.01)

- PaLM-E: [PaLM-E: An Embodied Multimodal Language Model](https://arxiv.org/pdf/2303.03378.pdf) (Google, 2023.03)

- LLaVA: [Visual Instruction Tuning](https://arxiv.org/pdf/2304.08485.pdf) (Microsoft, 2023.04), [github](https://github.com/haotian-liu/LLaVA)

- MiniGPT-4: [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://arxiv.org/pdf/2304.10592.pdf) (KAUST, 2023.04)

- mPLUG-Owl: [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://arxiv.org/pdf/2304.14178.pdf) (Alibaba, 2023.04)

- InstructBLIP: [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/pdf/2305.06500.pdf) (Salesforce, 2023.05)

### Speech

- AudioGPT: [AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head](https://arxiv.org/pdf/2304.12995.pdf) (ZJU, 2023.04, [github](https://github.com/AIGC-Audio/AudioGPT))

- SpeechGPT: [SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities](https://arxiv.org/pdf/2305.11000.pdf) (FUDAN, 2023.05, [github](https://0nutation.github.io/SpeechGPT.github.io/))