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
• Prefix-tuning: Optimizing Continuous Prompts For Generation (2021)
  Xiang Lisa Li, Percy Liang
  32.38
• 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
• Ultrafeedback: Boosting Language Models With Scaled AI Feedback (2023)
  Ganqu Cui, Lifan Yuan, Ning Ding, et al.
  27.73
• 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
• 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
• 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
• When Scaling Meets LLM Finetuning: The Effect Of Data, Model And Finetuning Method (2024)
  Biao Zhang, Zhongtao Liu, Colin Cherry, et al.
  25.34
• 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
• Imagebind-llm: Multi-modality Instruction Tuning (2023)
  Jiaming Han, Renrui Zhang, Wenqi Shao, et al.
  24.37
• 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
• 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
• Disc-lawllm: Fine-tuning Large Language Models For Intelligent Legal Services (2023)
  Shengbin Yue, Wei Chen, Siyuan Wang, et al.
  23.76
• Synthetic Data (almost) From Scratch: Generalized Instruction Tuning For Language Models (2024)
  Haoran Li, Qingxiu Dong, Zhengyang Tang, et al.
  23.56
• 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
• 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
• Expel: LLM Agents Are Experiential Learners (2023)
  Andrew Zhao, Daniel Huang, Quentin Xu, et al.
  23.10
• A Rank Stabilization Scaling Factor For Fine-tuning With Lora (2023)
  Damjan Kalajdzievski
  23.01
• LESS: Selecting Influential Data For Targeted Instruction Tuning (2024)
  Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, et al.
  22.96
• Zhongjing: Enhancing The Chinese Medical Capabilities Of Large Language Model Through Expert Feedback And Real-world Multi-turn Dialogue (2023)
  Songhua Yang, Hanjie Zhao, Senbin Zhu, et al.
  22.87
• Alpacare:instruction-tuned Large Language Models For Medical Application (2023)
  Xinlu Zhang, Chenxin Tian, Xianjun Yang, et al.
  22.41
• Does Fine-tuning Llms On New Knowledge Encourage Hallucinations? (2024)
  Zorik Gekhman, Gal Yona, Roee Aharoni, et al.
  22.36
• Fine-tuning Multimodal Llms To Follow Zero-shot Demonstrative Instructions (2023)
  Juncheng Li, Kaihang Pan, Zhiqi Ge, et al.
  22.33
• WISE: Rethinking The Knowledge Memory For Lifelong Model Editing Of Large Language Models (2024)
  Peng Wang, Zexi Li, Ningyu Zhang, et al.
  22.31
• R-tuning: Instructing Large Language Models To Say `I Don't Know' (2023)
  Hanning Zhang, Shizhe Diao, Yong Lin, et al.
  21.96
• Llm4decompile: Decompiling Binary Code With Large Language Models (2024)
  Hanzhuo Tan, Qi Luo, Jing Li, et al.
  21.86
• Making Large Language Models Perform Better In Knowledge Graph Completion (2023)
  Yichi Zhang, Zhuo Chen, Lingbing Guo, et al.
  21.74
• INTERS: Unlocking The Power Of Large Language Models In Search With Instruction Tuning (2024)
  Yutao Zhu, Peitian Zhang, Chenghao Zhang, et al.
  21.39
• LISA: Layerwise Importance Sampling For Memory-efficient Large Language Model Fine-tuning (2024)
  Rui Pan, Xiang Liu, Shizhe Diao, et al.
  21.31
• Vineppo: Refining Credit Assignment In RL Training Of Llms (2024)
  Amirhossein Kazemnejad, Milad Aghajohari, Eva Portelance, et al.
  21.27
• Understanding The Effects Of RLHF On LLM Generalisation And Diversity (2023)
  Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, et al.
  21.24
• Character-llm: A Trainable Agent For Role-playing (2023)
  Yunfan Shao, Linyang Li, Junqi Dai, et al.
  21.19
• Anchored Preference Optimization And Contrastive Revisions: Addressing Underspecification In Alignment (2024)
  Karel D'Oosterlinck, Winnie Xu, Chris Develder, et al.
  21.08
• Rethinking Machine Unlearning For Large Language Models (2024)
  Sijia Liu, Yuanshun Yao, Jinghan Jia, et al.
  21.08
• Higher Layers Need More Lora Experts (2024)
  Chongyang Gao, Kezhen Chen, Jinmeng Rao, et al.
  21.06
• Negative Preference Optimization: From Catastrophic Collapse To Effective Unlearning (2024)
  Ruiqi Zhang, Licong Lin, Yu Bai, et al.
  20.98
• Groundinggpt:language Enhanced Multi-modal Grounding Model (2024)
  Zhaowei Li, Qi Xu, Dong Zhang, et al.
  20.91
• PMET: Precise Model Editing In A Transformer (2023)
  Xiaopeng Li, Shasha Li, Shezheng Song, et al.
  20.90
• Smaug: Fixing Failure Modes Of Preference Optimisation With Dpo-positive (2024)
  Arka Pal, Deep Karkhanis, Samuel Dooley, et al.
  20.86
• Text-to-sql Empowered By Large Language Models: A Benchmark Evaluation (2023)
  Dawei Gao, Haibin Wang, Yaliang Li, et al.
  20.77