{"id":73276,"url":"https://github.com/thinkwee/awesome-llm-if","name":"awesome-llm-if","description":"An Awesome List to LLM Instruction Following","projects_count":37,"last_synced_at":"2026-08-25T11:00:30.256Z","repository":{"id":247584792,"uuid":"645157454","full_name":"thinkwee/Awesome-LLM-IF","owner":"thinkwee","description":"An Awesome List to LLM Instruction Following","archived":false,"fork":false,"pushed_at":"2024-10-21T05:55:19.000Z","size":195,"stargazers_count":8,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-07-17T15:26:10.418Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/thinkwee.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-05-25T03:43:38.000Z","updated_at":"2026-04-13T10:42:15.000Z","dependencies_parsed_at":"2024-10-16T09:01:13.825Z","dependency_job_id":"de57a2e8-023e-4010-9c9b-a07c7ca65ee8","html_url":"https://github.com/thinkwee/Awesome-LLM-IF","commit_stats":null,"previous_names":["thinkwee/awesome-llm-if"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/thinkwee/Awesome-LLM-IF","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thinkwee%2FAwesome-LLM-IF","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thinkwee%2FAwesome-LLM-IF/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thinkwee%2FAwesome-LLM-IF/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thinkwee%2FAwesome-LLM-IF/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/thinkwee","download_url":"https://codeload.github.com/thinkwee/Awesome-LLM-IF/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thinkwee%2FAwesome-LLM-IF/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":36322586,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-07-20T02:08:10.276Z","status":"online","status_checked_at":"2026-08-05T02:00:06.619Z","response_time":104,"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"}},"created_at":"2024-10-10T18:07:53.423Z","updated_at":"2026-08-25T11:00:30.256Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Uncategorized"],"sub_categories":["Uncategorized"],"readme":"\u003cp align=\"center\"\u003e\n  \u003cimg src='./logo.png' width=700\u003e\n\u003c/p\u003e\n\nExcellent **IF (Instruction Following)** capabilities are the foundation for building complex applications (such as [Tool Usage](https://github.com/thunlp/ToolLearningPapers) or [Multi-Agent System](https://thinkwee.top/multiagent_ebook/)) based on LLMs. This repository aims to provide a comprehensive list of papers, repositories, and other resources related to improving, evaluating, benchmarking, and theoretically analyzing instruction-following capabilities, in order to advance research in this field.\n\nThe repository is still under active construction, and we welcome everyone to collaborate and contribute!\n\n# Method\n- [DO LLMS “KNOW” INTERNALLY WHEN THEY FOLLOW INSTRUCTIONS?](https://arxiv.org/pdf/2410.14516)\n  - Cambridge, Apple\n  - In submission to ICLR 2025\n- [SELF-PLAY WITH EXECUTION FEEDBACK: IMPROVING INSTRUCTION-FOLLOWING CAPABILITIES OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2406.13542)\n  - Alibaba\n  - [AutoIF](https://github.com/QwenLM/AutoIF)  ![](https://img.shields.io/github/stars/QwenLM/AutoIF.svg)\n- [LESS: Selecting Influential Data for Targeted Instruction Tuning](https://arxiv.org/pdf/2402.04333)\n  - Princeton University, University of Washington\n  - ICML 2024\n  - [LESS](https://github.com/princeton-nlp/less)  ![](https://img.shields.io/github/stars/princeton-nlp/less.svg)\n- [WizardLM: Empowering Large Language Models to Follow Complex Instructions](https://arxiv.org/pdf/2304.12244)\n  - Microsoft, Peking University\n  - ICLR 2024\n  - [WizardLM](https://github.com/nlpxucan/WizardLM)  ![](https://img.shields.io/github/stars/nlpxucan/WizardLM.svg)\n- [Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models](https://arxiv.org/pdf/2402.11532)\n  - University of Minnesota, Amazon AGI, Grammarly\n- [Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://arxiv.org/pdf/2406.14491)\n  - Microsoft Research, Tsinghua University\n  - [LMOps](https://github.com/microsoft/LMOps)  ![](https://img.shields.io/github/stars/microsoft/LMOps.svg)\n\n# Evaluation\n- [StructEval: Benchmarking LLMs' Capabilities to Generate Structural Outputs](https://arxiv.org/abs/2505.20139)\n  - University of Waterloo, University of Toronto, HKUST, Shanghai University, Vector Institute, University of British Columbia\n  - TMLR 2025\n  - [StructEval](https://github.com/TIGER-AI-Lab/StructEval)  ![](https://img.shields.io/github/stars/TIGER-AI-Lab/StructEval.svg)\n- [Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators](https://arxiv.org/pdf/2404.04475)\n  - Stanford University, Independent Researcher\n  - [alpaca_eval](https://github.com/tatsu-lab/alpaca_eval)  ![](https://img.shields.io/github/stars/tatsu-lab/alpaca_eval.svg)\n- [INFOBENCH: Evaluating Instruction Following Ability in Large Language Models](https://arxiv.org/pdf/2401.03601)\n  - Tencent AI Lab, Seattle; University of Central Florida; Emory University; University of Georgia; Shanghai Jiao Tong University\n  - [InfoBench](https://github.com/qinyiwei/InfoBench)  ![](https://img.shields.io/github/stars/qinyiwei/InfoBench.svg)\n- [STRUC-BENCH: Are Large Language Models Good at Generating Complex Structured Tabular Data?](https://aclanthology.org/2024.naacl-short.2.pdf)\n  - Yale University, Zhejiang University, New York University\n  - NAACL 2024\n  - [Struc-Bench](https://github.com/gersteinlab/Struc-Bench)  ![](https://img.shields.io/github/stars/gersteinlab/Struc-Bench.svg)\n- [FOFO: A Benchmark to Evaluate LLMs’ Format-Following Capability](https://arxiv.org/pdf/2402.18667)\n  - Salesforce Research, University of Illinois at Chicago, Pennsylvania State University\n  - [FoFo](https://github.com/SalesforceAIResearch/FoFo)  ![](https://img.shields.io/github/stars/SalesforceAIResearch/FoFo.svg)\n- [AlignBench: Benchmarking Chinese Alignment of Large Language Models](https://arxiv.org/pdf/2311.18743)\n  - Tsinghua University, Zhipu AI, Renmin University of China, Sichuan University, Lehigh University\n  - [AlignBench](https://github.com/THUDM/AlignBench)  ![](https://img.shields.io/github/stars/THUDM/AlignBench.svg)\n- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://proceedings.neurips.cc/paper_files/paper/2023/file/91f18a1287b398d378ef22505bf41832-Paper-Datasets_and_Benchmarks.pdf)\n  - UC Berkeley, UC San Diego, Carnegie Mellon University, Stanford, MBZUAI\n  - NeuralPS 2023\n  - [llm_judge](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge)  ![](https://img.shields.io/github/stars/lm-sys/FastChat.svg)\n- [Benchmarking Complex Instruction-Following with Multiple Constraints Composition](https://arxiv.org/pdf/2407.03978)\n  - Tsinghua, Zhipu, China University of Geosciences, Central China Normal University\n  - [ComplexBench](https://github.com/thu-coai/ComplexBench)  ![](https://img.shields.io/github/stars/thu-coai/ComplexBench.svg)\n- [EVALUATING LARGE LANGUAGE MODELS AT EVALUATING INSTRUCTION FOLLOWING](https://arxiv.org/pdf/2310.07641)\n  - Tsinghua, Princeton, UIUC\n  - ICLR 2024\n  - [LLMBar](https://github.com/lyogavin/Anima)  ![](https://img.shields.io/github/stars/princeton-nlp/LLMBar.svg)\n- [Instruction-Following Evaluation for Large Language Models](https://arxiv.org/pdf/2311.07911)\n  - Google, Yale\n  - [instruction_following_eval](https://github.com/google-research/google-research/tree/master/instruction_following_eval) ![](https://img.shields.io/github/stars/google-research/google-research.svg)\n- [FollowEval: A Multi-Dimensional Benchmark for Assessing the Instruction-Following Capability of Large Language Models](https://arxiv.org/pdf/2311.09829)\n  - Lenovo, TJU\n- [Can Large Language Models Understand Real-World Complex Instructions?](https://arxiv.org/pdf/2309.09150)\n  - Fudan, ECNU\n  - AAAI 2024\n  - [CELLO](https://github.com/Abbey4799/CELLO)  ![](https://img.shields.io/github/stars/Abbey4799/CELLO.svg)\n- [FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models](https://arxiv.org/pdf/2310.20410)\n  - HKUST, Huawei\n  - ACL 2024\n  - [FollowBench](https://github.com/YJiangcm/FollowBench)  ![](https://img.shields.io/github/stars/YJiangcm/FollowBench.svg)\n- [Evaluating Large Language Models on Controlled Generation Tasks](https://arxiv.org/pdf/2310.14542)\n  - USC, UC, ETH, Amazon, Deepmind\n  - [llm-controlgen](https://github.com/sunjiao123sun/llm-controlgen)  ![](https://img.shields.io/github/stars/sunjiao123sun/llm-controlgen.svg)\n\n# Contributors\n\u003ca href=\"https://github.com/HqWu-HITCS\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/29895268?v=4\" alt=\"图片描述\" style=\"width:5%;\"/\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/yupeijei1997\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/39047479?v=4\" alt=\"图片描述\" style=\"width:5%;\"/\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/thinkwee\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/11889052?v=4\" alt=\"图片描述\" style=\"width:5%;\"/\u003e\u003c/a\u003e\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/thinkwee%2Fawesome-llm-if/projects"}