Key papers

• Efficient Memory Management For Large Language Model Serving With Pagedattention (2023)
  Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, et al.
  46.56
• Prefix-tuning: Optimizing Continuous Prompts For Generation (2021)
  Xiang Lisa Li, Percy Liang
  32.38
• Adaptive-rag: Learning To Adapt Retrieval-augmented Large Language Models Through Question Complexity (2024)
  Soyeong Jeong, Jinheon Baek, Sukmin Cho, et al.
  29.58
• Scaling Synthetic Data Creation With 1,000,000,000 Personas (2024)
  Tao Ge, Xin Chan, Xiaoyang Wang, et al.
  29.19
• Multihop-rag: Benchmarking Retrieval-augmented Generation For Multi-hop Queries (2024)
  Yixuan Tang, Yi Yang
  27.60
• Duoattention: Efficient Long-context LLM Inference With Retrieval And Streaming Heads (2024)
  Guangxuan Xiao, Jiaming Tang, Jingwei Zuo, et al.
  27.44
• Flashrag: A Modular Toolkit For Efficient Retrieval-augmented Generation Research (2024)
  Jiajie Jin, Yutao Zhu, Guanting Dong, et al.
  26.95
• Saullm-7b: A Pioneering Large Language Model For Law (2024)
  Pierre Colombo, Telmo Pessoa Pires, Malik Boudiaf, et al.
  26.00
• Mindmap: Knowledge Graph Prompting Sparks Graph Of Thoughts In Large Language Models (2023)
  Yilin Wen, Zifeng Wang, Jimeng Sun
  25.51
• Ragas: Automated Evaluation Of Retrieval Augmented Generation (2023)
  Shahul Es, Jithin James, Luis Espinosa-Anke, et al.
  24.85
• Layoutllm: Layout Instruction Tuning With Large Language Models For Document Understanding (2024)
  Chuwei Luo, Yufan Shen, Zhaoqing Zhu, et al.
  24.66
• A Systematic Survey Of Prompt Engineering In Large Language Models: Techniques And Applications (2024)
  Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, et al.
  24.27
• 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
• Patchscopes: A Unifying Framework For Inspecting Hidden Representations Of Language Models (2024)
  Asma Ghandeharioun, Avi Caciularu, Adam Pearce, et al.
  23.87
• Disc-lawllm: Fine-tuning Large Language Models For Intelligent Legal Services (2023)
  Shengbin Yue, Wei Chen, Siyuan Wang, et al.
  23.76
• Tool Learning With Large Language Models: A Survey (2024)
  Changle Qu, Sunhao Dai, Xiaochi Wei, et al.
  23.52
• Graph Chain-of-thought: Augmenting Large Language Models By Reasoning On Graphs (2024)
  Bowen Jin, Chulin Xie, Jiawei Zhang, et al.
  23.21
• Expel: LLM Agents Are Experiential Learners (2023)
  Andrew Zhao, Daniel Huang, Quentin Xu, et al.
  23.10
• Fact, Fetch, And Reason: A Unified Evaluation Of Retrieval-augmented Generation (2024)
  Satyapriya Krishna, Kalpesh Krishna, Anhad Mohananey, et al.
  22.96
• Visrag: Vision-based Retrieval-augmented Generation On Multi-modality Documents (2024)
  Shi Yu, Chaoyue Tang, Bokai Xu, et al.
  22.87
• Reasoning On Graphs: Faithful And Interpretable Large Language Model Reasoning (2023)
  Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, et al.
  22.59
• Does Fine-tuning Llms On New Knowledge Encourage Hallucinations? (2024)
  Zorik Gekhman, Gal Yona, Roee Aharoni, et al.
  22.36
• Memorag: Boosting Long Context Processing With Global Memory-enhanced Retrieval Augmentation (2024)
  Hongjin Qian, Zheng Liu, Peitian Zhang, et al.
  22.21
• Ragtruth: A Hallucination Corpus For Developing Trustworthy Retrieval-augmented Language Models (2024)
  Cheng Niu, Yuanhao Wu, Juno Zhu, et al.
  22.10
• AST-T5: Structure-aware Pretraining For Code Generation And Understanding (2024)
  Linyuan Gong, Mostafa Elhoushi, Alvin Cheung
  21.95
• A Survey On RAG Meeting Llms: Towards Retrieval-augmented Large Language Models (2024)
  Wenqi Fan, Yujuan Ding, Liangbo Ning, et al.
  21.90
• From RAG To Memory: Non-parametric Continual Learning For Large Language Models (2025)
  Bernal Jiménez Gutiérrez, Yiheng Shu, Weijian Qi, et al.
  21.22
• A Simple And Effective \(L_2\) Norm-based Strategy For KV Cache Compression (2024)
  Alessio Devoto, Yu Zhao, Simone Scardapane, et al.
  20.70
• The Good And The Bad: Exploring Privacy Issues In Retrieval-augmented Generation (RAG) (2024)
  Shenglai Zeng, Jiankun Zhang, Pengfei He, et al.
  20.64
• Disc-finllm: A Chinese Financial Large Language Model Based On Multiple Experts Fine-tuning (2023)
  Wei Chen, Qiushi Wang, Zefei Long, et al.
  20.11
• Jatmo: Prompt Injection Defense By Task-specific Finetuning (2023)
  Julien Piet, Maha Alrashed, Chawin Sitawarin, et al.
  20.06
• Benchmarking Uncertainty Quantification Methods For Large Language Models With Lm-polygraph (2024)
  Roman Vashurin, Ekaterina Fadeeva, Artem Vazhentsev, et al.
  20.04
• Can Large Language Models Understand Real-world Complex Instructions? (2023)
  Qianyu He, Jie Zeng, Wenhao Huang, et al.
  19.91
• Continual Pre-training For Cross-lingual LLM Adaptation: Enhancing Japanese Language Capabilities (2024)
  Kazuki Fujii, Taishi Nakamura, Mengsay Loem, et al.
  19.81
• Fact-checking The Output Of Large Language Models Via Token-level Uncertainty Quantification (2024)
  Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, et al.
  19.78
• Chain-of-note: Enhancing Robustness In Retrieval-augmented Language Models (2023)
  Wenhao Yu, Hongming Zhang, Xiaoman Pan, et al.
  19.67
• Clasheval: Quantifying The Tug-of-war Between An Llm's Internal Prior And External Evidence (2024)
  Kevin Wu, Eric Wu, James Zou
  19.62
• Stacking Your Transformers: A Closer Look At Model Growth For Efficient LLM Pre-training (2024)
  Wenyu Du, Tongxu Luo, Zihan Qiu, et al.
  19.56
• Omnieval: An Omnidirectional And Automatic RAG Evaluation Benchmark In Financial Domain (2024)
  Shuting Wang, Jiejun Tan, Zhicheng Dou, et al.
  19.52
• Magicpig: LSH Sampling For Efficient LLM Generation (2024)
  Zhuoming Chen, Ranajoy Sadhukhan, Zihao Ye, et al.
  19.39
• From Words To Watts: Benchmarking The Energy Costs Of Large Language Model Inference (2023)
  Siddharth Samsi, Dan Zhao, Joseph McDonald, et al.
  18.71
• Repairagent: An Autonomous, Llm-based Agent For Program Repair (2024)
  Islem Bouzenia, Premkumar Devanbu, Michael Pradel
  18.60
• Preference Fine-tuning Of Llms Should Leverage Suboptimal, On-policy Data (2024)
  Fahim Tajwar, Anikait Singh, Archit Sharma, et al.
  18.58
• Nphardeval: Dynamic Benchmark On Reasoning Ability Of Large Language Models Via Complexity Classes (2023)
  Lizhou Fan, Wenyue Hua, Lingyao Li, et al.
  18.49
• DRAGIN: Dynamic Retrieval Augmented Generation Based On The Information Needs Of Large Language Models (2024)
  Weihang Su, Yichen Tang, Qingyao Ai, et al.
  18.26
• Llasmol: Advancing Large Language Models For Chemistry With A Large-scale, Comprehensive, High-quality Instruction Tuning Dataset (2024)
  Botao Yu, Frazier N. Baker, Ziqi Chen, et al.
  18.23
• Draw-and-understand: Leveraging Visual Prompts To Enable Mllms To Comprehend What You Want (2024)
  Weifeng Lin, Xinyu Wei, Ruichuan An, et al.
  18.04
• Autoflow: Automated Workflow Generation For Large Language Model Agents (2024)
  Zelong Li, Shuyuan Xu, Kai Mei, et al.
  17.75
• Slim-llm: Salience-driven Mixed-precision Quantization For Large Language Models (2024)
  Wei Huang, Haotong Qin, Yangdong Liu, et al.
  17.72
• Reevo: Large Language Models As Hyper-heuristics With Reflective Evolution (2024)
  Haoran Ye, Jiarui Wang, Zhiguang Cao, et al.
  17.68
• DALK: Dynamic Co-augmentation Of Llms And KG To Answer Alzheimer's Disease Questions With Scientific Literature (2024)
  Dawei Li, Shu Yang, Zhen Tan, et al.
  17.63
• Similarity Is Not All You Need: Endowing Retrieval Augmented Generation With Multi Layered Thoughts (2024)
  Chunjing Gan, Dan Yang, Binbin Hu, et al.
  17.56
• A Survey Of Graph Retrieval-augmented Generation For Customized Large Language Models (2025)
  Qinggang Zhang, Shengyuan Chen, Yuanchen Bei, et al.
  17.43
• NOLA: Compressing Lora Using Linear Combination Of Random Basis (2023)
  Soroush Abbasi Koohpayegani, Kl Navaneet, Parsa Nooralinejad, et al.
  17.37
• Leveraging Large Language Models For Sequential Recommendation (2023)
  Jesse Harte, Wouter Zorgdrager, Panos Louridas, et al.
  17.24
• Instructretro: Instruction Tuning Post Retrieval-augmented Pretraining (2023)
  Boxin Wang, Wei Ping, Lawrence McAfee, et al.
  17.13
• Mm-embed: Universal Multimodal Retrieval With Multimodal Llms (2024)
  Sheng-Chieh Lin, Chankyu Lee, Mohammad Shoeybi, et al.
  17.06
• Repliqa: A Question-answering Dataset For Benchmarking Llms On Unseen Reference Content (2024)
  Joao Monteiro, Pierre-Andre Noel, Etienne Marcotte, et al.
  17.06
• Gpt-fathom: Benchmarking Large Language Models To Decipher The Evolutionary Path Towards GPT-4 And Beyond (2023)
  Shen Zheng, Yuyu Zhang, Yijie Zhu, et al.
  16.96
• Empowering Large Language Models To Set Up A Knowledge Retrieval Indexer Via Self-learning (2024)
  Xun Liang, Simin Niu, Zhiyu Li, et al.
  16.91