Awesome Fine-Tuning

Key papers

• Lora: Low-rank Adaptation Of Large Language Models (2021)
  Edward J. Hu, Yelong Shen, Phillip Wallis, et al.
  48.60
• Zephyr: Direct Distillation Of LM Alignment (2023)
  Lewis Tunstall, Edward Beeching, Nathan Lambert, et al.
  38.56
• Training Language Models To Follow Instructions With Human Feedback (2022)
  Long Ouyang, Jeff Wu, Xu Jiang, et al.
  36.92
• Qlora: Efficient Finetuning Of Quantized Llms (2023)
  Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, et al.
  36.23
• Minicpm: Unveiling The Potential Of Small Language Models With Scalable Training Strategies (2024)
  Shengding Hu, Yuge Tu, Xu Han, et al.
  36.01
• Agenttuning: Enabling Generalized Agent Abilities For Llms (2023)
  Aohan Zeng, Mingdao Liu, Rui Lu, et al.
  32.67
• Prefix-tuning: Optimizing Continuous Prompts For Generation (2021)
  Xiang Lisa Li, Percy Liang
  32.38
• Judgelm: Fine-tuned Large Language Models Are Scalable Judges (2023)
  Lianghui Zhu, Xinggang Wang, Xinlong Wang
  31.75
• The Fineweb Datasets: Decanting The Web For The Finest Text Data At Scale (2024)
  Guilherme Penedo, Hynek Kydlíček, Loubna Ben Allal, et al.
  31.68
• SPHINX: The Joint Mixing Of Weights, Tasks, And Visual Embeddings For Multi-modal Large Language Models (2023)
  Ziyi Lin, Chris Liu, Renrui Zhang, et al.
  31.35
• Step-dpo: Step-wise Preference Optimization For Long-chain Reasoning Of Llms (2024)
  Xin Lai, Zhuotao Tian, Yukang Chen, et al.
  31.31
• Adaptive-rag: Learning To Adapt Retrieval-augmented Large Language Models Through Question Complexity (2024)
  Soyeong Jeong, Jinheon Baek, Sukmin Cho, et al.
  29.58
• Qa-lora: Quantization-aware Low-rank Adaptation Of Large Language Models (2023)
  Yuhui Xu, Lingxi Xie, Xiaotao Gu, et al.
  29.52
• Prometheus: Inducing Fine-grained Evaluation Capability In Language Models (2023)
  Seungone Kim, Jamin Shin, Yejin Cho, et al.
  28.76
• Ultrafeedback: Boosting Language Models With Scaled AI Feedback (2023)
  Ganqu Cui, Lifan Yuan, Ning Ding, et al.
  27.73
• Contrastive Preference Optimization: Pushing The Boundaries Of LLM Performance In Machine Translation (2024)
  Haoran Xu, Amr Sharaf, Yunmo Chen, et al.
  27.70
• Aya Model: An Instruction Finetuned Open-access Multilingual Language Model (2024)
  Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.
  27.57
• Magicoder: Empowering Code Generation With Oss-instruct (2023)
  Yuxiang Wei, Zhe Wang, Jiawei Liu, et al.
  27.43
• Internlm2 Technical Report (2024)
  Zheng Cai, Maosong Cao, Haojiong Chen, et al.
  27.03
• Longalign: A Recipe For Long Context Alignment Of Large Language Models (2024)
  Yushi Bai, Xin Lv, Jiajie Zhang, et al.
  26.93
• Dola: Decoding By Contrasting Layers Improves Factuality In Large Language Models (2023)
  Yung-Sung Chuang, Yujia Xie, Hongyin Luo, et al.
  26.86
• Datadreamer: A Tool For Synthetic Data Generation And Reproducible LLM Workflows (2024)
  Ajay Patel, Colin Raffel, Chris Callison-Burch
  26.77
• A Survey On Large Language Models For Code Generation (2024)
  Juyong Jiang, Fan Wang, Jiasi Shen, et al.
  26.64
• Learning From Mistakes Makes LLM Better Reasoner (2023)
  Shengnan An, Zexiong Ma, Zeqi Lin, et al.
  26.19
• The Unlocking Spell On Base Llms: Rethinking Alignment Via In-context Learning (2023)
  Bill Yuchen Lin, Abhilasha Ravichander, Ximing Lu, et al.
  26.03
• Mammoth: Building Math Generalist Models Through Hybrid Instruction Tuning (2023)
  Xiang Yue, Xingwei Qu, Ge Zhang, et al.
  25.94
• Direct Nash Optimization: Teaching Language Models To Self-improve With General Preferences (2024)
  Corby Rosset, Ching-An Cheng, Arindam Mitra, et al.
  25.73
• Fine-tuning Language Models For Factuality (2023)
  Katherine Tian, Eric Mitchell, Huaxiu Yao, et al.
  25.62
• Freshllms: Refreshing Large Language Models With Search Engine Augmentation (2023)
  Tu Vu, Mohit Iyyer, Xuezhi Wang, et al.
  25.58
• Agent-flan: Designing Data And Methods Of Effective Agent Tuning For Large Language Models (2024)
  Zehui Chen, Kuikun Liu, Qiuchen Wang, et al.
  25.55
• When Scaling Meets LLM Finetuning: The Effect Of Data, Model And Finetuning Method (2024)
  Biao Zhang, Zhongtao Liu, Colin Cherry, et al.
  25.34
• Llamax: Scaling Linguistic Horizons Of LLM By Enhancing Translation Capabilities Beyond 100 Languages (2024)
  Yinquan Lu, Wenhao Zhu, Lei Li, et al.
  25.17
• Detoxifying Large Language Models Via Knowledge Editing (2024)
  Mengru Wang, Ningyu Zhang, Ziwen Xu, et al.
  25.02
• Toward Self-improvement Of Llms Via Imagination, Searching, And Criticizing (2024)
  Ye Tian, Baolin Peng, Linfeng Song, et al.
  24.80
• Continual Learning Of Large Language Models: A Comprehensive Survey (2024)
  Haizhou Shi, Zihao Xu, Hengyi Wang, et al.
  24.79
• Layoutllm: Layout Instruction Tuning With Large Language Models For Document Understanding (2024)
  Chuwei Luo, Yufan Shen, Zhaoqing Zhu, et al.
  24.66
• Self-play With Execution Feedback: Improving Instruction-following Capabilities Of Large Language Models (2024)
  Guanting Dong, Keming Lu, Chengpeng Li, et al.
  24.59
• Personalized Soups: Personalized Large Language Model Alignment Via Post-hoc Parameter Merging (2023)
  Joel Jang, Seungone Kim, Bill Yuchen Lin, et al.
  24.50
• Imagebind-llm: Multi-modality Instruction Tuning (2023)
  Jiaming Han, Renrui Zhang, Wenqi Shao, et al.
  24.37
• Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! (2023)
  Xiangyu Qi, Yi Zeng, Tinghao Xie, et al.
  24.31
• Federatedscope-llm: A Comprehensive Package For Fine-tuning Large Language Models In Federated Learning (2023)
  Weirui Kuang, Bingchen Qian, Zitao Li, et al.
  24.26
• Compact Language Models Via Pruning And Knowledge Distillation (2024)
  Saurav Muralidharan, Sharath Turuvekere Sreenivas, Raviraj Joshi, et al.
  24.15
• Platypus: Quick, Cheap, And Powerful Refinement Of Llms (2023)
  Ariel N. Lee, Cole J. Hunter, Nataniel Ruiz
  24.03
• Lm-cocktail: Resilient Tuning Of Language Models Via Model Merging (2023)
  Shitao Xiao, Zheng Liu, Peitian Zhang, et al.
  23.85
• Chatglm-math: Improving Math Problem-solving In Large Language Models With A Self-critique Pipeline (2024)
  Yifan Xu, Xiao Liu, Xinghan Liu, et al.
  23.84
• Llama-moe: Building Mixture-of-experts From Llama With Continual Pre-training (2024)
  Tong Zhu, Xiaoye Qu, Daize Dong, et al.
  23.81
• Nash Learning From Human Feedback (2023)
  Rémi Munos, Michal Valko, Daniele Calandriello, et al.
  23.79
• Disc-lawllm: Fine-tuning Large Language Models For Intelligent Legal Services (2023)
  Shengbin Yue, Wei Chen, Siyuan Wang, et al.
  23.76
• Self-exploring Language Models: Active Preference Elicitation For Online Alignment (2024)
  Shenao Zhang, Donghan Yu, Hiteshi Sharma, et al.
  23.63
• Synthetic Data (almost) From Scratch: Generalized Instruction Tuning For Language Models (2024)
  Haoran Li, Qingxiu Dong, Zhengyang Tang, et al.
  23.56
• Large Language Model Unlearning Via Embedding-corrupted Prompts (2024)
  Chris Yuhao Liu, Yaxuan Wang, Jeffrey Flanigan, et al.
  23.55
• Instruction Tuning For Large Language Models: A Survey (2023)
  Shengyu Zhang, Linfeng Dong, Xiaoya Li, et al.
  23.46
• Two Tales Of Persona In Llms: A Survey Of Role-playing And Personalization (2024)
  Yu-Min Tseng, Yu-Chao Huang, Teng-Yun Hsiao, et al.
  23.45
• Adapting Large Language Models By Integrating Collaborative Semantics For Recommendation (2023)
  Bowen Zheng, Yupeng Hou, Hongyu Lu, et al.
  23.45
• Table-gpt: Table-tuned GPT For Diverse Table Tasks (2023)
  Peng Li, Yeye He, Dror Yashar, et al.
  23.44
• MMC: Advancing Multimodal Chart Understanding With Large-scale Instruction Tuning (2023)
  Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, et al.
  23.28
• A Paradigm Shift In Machine Translation: Boosting Translation Performance Of Large Language Models (2023)
  Haoran Xu, Young Jin Kim, Amr Sharaf, et al.
  23.20
• Chipnemo: Domain-adapted Llms For Chip Design (2023)
  Mingjie Liu, Teodor-Dumitru Ene, Robert Kirby, et al.
  23.12
• Expel: LLM Agents Are Experiential Learners (2023)
  Andrew Zhao, Daniel Huang, Quentin Xu, et al.
  23.10
• Let The Expert Stick To His Last: Expert-specialized Fine-tuning For Sparse Architectural Large Language Models (2024)
  Zihan Wang, Deli Chen, Damai Dai, et al.
  23.04