{"id":13545853,"url":"https://github.com/wenet-e2e/llm-papers","last_synced_at":"2025-10-17T09:11:30.857Z","repository":{"id":172004623,"uuid":"648702403","full_name":"wenet-e2e/llm-papers","owner":"wenet-e2e","description":"List of Large Lanugage Model Papers","archived":false,"fork":false,"pushed_at":"2023-06-05T14:59:02.000Z","size":11,"stargazers_count":57,"open_issues_count":0,"forks_count":3,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-02-27T13:04:16.734Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/wenet-e2e.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2023-06-02T15:36:34.000Z","updated_at":"2025-02-25T13:40:56.000Z","dependencies_parsed_at":"2023-07-29T21:00:17.692Z","dependency_job_id":null,"html_url":"https://github.com/wenet-e2e/llm-papers","commit_stats":null,"previous_names":["wenet-e2e/llm-papers"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/wenet-e2e/llm-papers","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wenet-e2e%2Fllm-papers","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wenet-e2e%2Fllm-papers/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wenet-e2e%2Fllm-papers/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wenet-e2e%2Fllm-papers/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/wenet-e2e","download_url":"https://codeload.github.com/wenet-e2e/llm-papers/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wenet-e2e%2Fllm-papers/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":267638774,"owners_count":24119764,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-07-29T02:00:12.549Z","response_time":2574,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-01T12:00:23.552Z","updated_at":"2025-10-17T09:11:30.754Z","avatar_url":"https://github.com/wenet-e2e.png","language":null,"funding_links":[],"categories":["🌟 Topics"],"sub_categories":["LLM"],"readme":"# llm-papers\nList of Large Lanugage Model Papers\n\n\n## GPTs by OpenAI\n\n- GPT-1: [Improving Language Understanding by Generative Pre-Training](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) (2018)\n- GPT-2: [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) (2019)\n- GPT-3: [Language Models are Few-Shot Learners](https://arxiv.org/pdf/2005.14165.pdf) (2020)\n- InstructGPT: [Training language models to follow instructions with human feedback](https://arxiv.org/pdf/2203.02155.pdf) (2022)\n- ChatGPT: [Introducing ChatGPT](https://openai.com/blog/chatgpt), blog (2022)\n- GPT-4: [GPT-4 Technical Report](https://arxiv.org/pdf/2303.08774.pdf) (2023)\n\n\n## Prompt\n\n- Chain-of-Thought: [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/pdf/2201.11903.pdf) (Google, NeurIPS, 2022)\n- ReAct: [REACT: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS](https://arxiv.org/pdf/2210.03629.pdf) (Google, ICLR, 2023)\n- Self-Ask: [MEASURING AND NARROWING THE COMPOSITIONALITY GAP IN LANGUAGE MODELS](https://arxiv.org/pdf/2210.03350.pdf) (UW, 2023)\n\n## Finetune\n- Prompt Tuning: [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/pdf/2104.08691.pdf) (Google, EMNLP, 2021)\n- Prefix Tuning: [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://arxiv.org/pdf/2101.00190.pdf) (Stanford, IJCNLP, 2021)\n- LoRA: [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/pdf/2106.09685.pdf) (Microsoft, ICLR, 2022)\n- 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)\n- 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)\n- AdaLoRA: [Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning](https://arxiv.org/pdf/2303.10512.pdf) (Georgia Tech, ICLR, 2023)\n- QLoRA: [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/pdf/2305.14314.pdf) (UW, Submitted to NeurIPS, 2023)\n\n## Multi Modality\n\n### Image\n\n- 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)\n- PaLM-E: [PaLM-E: An Embodied Multimodal Language Model](https://arxiv.org/pdf/2303.03378.pdf) (Google, 2023.03)\n- LLaVA: [Visual Instruction Tuning](https://arxiv.org/pdf/2304.08485.pdf) (Microsoft, 2023.04), [github](https://github.com/haotian-liu/LLaVA)\n- MiniGPT-4: [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://arxiv.org/pdf/2304.10592.pdf) (KAUST, 2023.04)\n- mPLUG-Owl: [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://arxiv.org/pdf/2304.14178.pdf) (Alibaba, 2023.04)\n- InstructBLIP: [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://arxiv.org/pdf/2305.06500.pdf) (Salesforce, 2023.05)\n\n### Speech\n\n- 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))\n- 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/))\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwenet-e2e%2Fllm-papers","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwenet-e2e%2Fllm-papers","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwenet-e2e%2Fllm-papers/lists"}