knowledge_distillation.md

Knowledge Distillation

Title & Authors	Introduction	Links
Specializing Smaller Language Models towards Multi-Step Reasoning Yao Fu, Hao Peng, Litu Ou, Ashish Sabharwal, Tushar Khot		Github Paper
Distilling Script Knowledge from Large Language Models for Constrained Language Planning Siyu Yuan, Jiangjie Chen, Ziquan Fu, Xuyang Ge, Soham Shah, Charles Robert Jankowski, Yanghua Xiao, Deqing Yang		Github Paper
SCOTT: Self-Consistent Chain-of-Thought Distillation Peifeng Wang, Zhengyang Wang, Zheng Li, Yifan Gao, Bing Yin, Xiang Ren		Paper
DISCO: Distilling Counterfactuals with Large Language Models Zeming Chen, Qiyue Gao, Antoine Bosselut, Ashish Sabharwal, Kyle Richardson		Github Paper
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation Chandra Bhagavatula, Jena D. Hwang, Doug Downey, Ronan Le Bras, Ximing Lu, Lianhui Qin, Keisuke Sakaguchi, Swabha Swayamdipta, Peter West, Yejin Choi		Github Paper Project
Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step Liunian Harold Li, Jack Hessel, Youngjae Yu, Xiang Ren, Kai-Wei Chang, Yejin Choi		Github Paper
Can Language Models Teach? Teacher Explanations Improve Student Performance via Theory of Mind Swarnadeep Saha, Peter Hase, and Mohit Bansal		Github Paper
Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents Hyungjoo Chae, Yongho Song, Kai Tzu-iunn Ong, Taeyoon Kwon, Minjin Kim, Youngjae Yu, Dongha Lee, Dongyeop Kang, Jinyoung Yeo		Paper
PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation Gaurav Sahu, Olga Vechtomova, Dzmitry Bahdanau, Issam H. Laradji		Github Paper
Turning Dust into Gold: Distilling Complex Reasoning Capabilities from LLMs by Leveraging Negative Data Yiwei Li, Peiwen Yuan, Shaoxiong Feng, Boyuan Pan, Bin Sun, Xinglin Wang, Heda Wang, Kan Li		Github Paper
Democratizing Reasoning Ability: Tailored Learning from Large Language Model Zhaoyang Wang, Shaohan Huang, Yuxuan Liu, Jiahai Wang, Minghui Song, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang		Github Paper
GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model Shicheng Tan, Weng Lam Tam, Yuanchun Wang, Wenwen Gong, Yang Yang, Hongyin Tang, Keqing He, Jiahao Liu, Jingang Wang, Shu Zhao, Peng Zhang, Jie Tang		Github Paper
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister		Github Paper
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression Jiduan Liu, Jiahao Liu, Qifan Wang, Jingang Wang, Xunliang Cai, Dongyan Zhao, Ran Lucien Wang, Rui Yan		Paper
Cache me if you Can: an Online Cost-aware Teacher-Student framework to Reduce the Calls to Large Language Models Ilias Stogiannidis, Stavros Vassos, Prodromos Malakasiotis, Ion Androutsopoulos		Github Paper
Efficiently Distilling LLMs for Edge Applications Achintya Kundu, Fabian Lim, Aaron Chew, Laura Wynter, Penny Chong, Rhui Dih Lee		Paper
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions Minghao Wu, Abdul Waheed, Chiyu Zhang, Muhammad Abdul-Mageed, Alham Fikri Aji		Github paper
Knowledge Distillation of Large Language Models Yuxian Gu, Li Dong, Furu Wei, Minlie Huang		Github Paper
Teaching Small Language Models to Reason Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, Eric Malmi, Aliaksei Severyn.		Paper
Large Language Model Distillation Doesn't Need a Teacher Ananya Harsh Jha, Dirk Groeneveld, Emma Strubell, Iz Beltagy		Github paper
The False Promise of Imitating Proprietary LLMs Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, Dawn Song		Paper
Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing Jaehun Jung, Peter West, Liwei Jiang, Faeze Brahman, Ximing Lu, Jillian Fisher, Taylor Sorensen, Yejin Choi		Github paper
PaD: Program-aided Distillation Specializes Large Models in Reasoning Xuekai Zhu, Biqing Qi, Kaiyan Zhang, Xingwei Long, Bowen Zhou		Paper
RLCD: Reinforcement Learning from Contrast Distillation for Language Model Alignment Kevin Yang, Dan Klein, Asli Celikyilmaz, Nanyun Peng, Yuandong Tian		Paper
Sci-CoT: Leveraging Large Language Models for Enhanced Knowledge Distillation in Small Models for Scientific QA Yuhan Ma, Haiqi Jiang, Chenyou Fan		Paper
UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition Wenxuan Zhou, Sheng Zhang, Yu Gu, Muhao Chen, Hoifung Poon		Github Paper Project
Baby Llama: knowledge distillation from an ensemble of teachers trained on a small dataset with no performance penalty Inar Timiryasov, Jean-Loup Tastet		Github Paper
DistillSpec: Improving Speculative Decoding via Knowledge Distillation Yongchao Zhou, Kaifeng Lyu, Ankit Singh Rawat, Aditya Krishna Menon, Afshin Rostamizadeh, Sanjiv Kumar, Jean-François Kagy, Rishabh Agarwal		Paper
Zephyr: Direct Distillation of LM Alignment Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, Nathan Sarrazin, Omar Sanseviero, Alexander M. Rush, Thomas Wolf		Github Paper
Towards the Law of Capacity Gap in Distilling Language Models Chen Zhang, Dawei Song, Zheyu Ye, Yan Gao		Github Paper
Unlock the Power: Competitive Distillation for Multi-Modal Large Language Models Xinwei Li, Li Lin, Shuai Wang, Chen Qian		Paper
Mixed Distillation Helps Smaller Language Model Better Reasoning Li Chenglin, Chen Qianglong, Wang Caiyu, Zhang Yin		Paper
Distilling Event Sequence Knowledge From Large Language Models Somin Wadhwa, Oktie Hassanzadeh, Debarun Bhattacharjya, Ken Barker, Jian Ni		Paper
Knowledge Distillation for Closed-Source Language Models Hongzhan Chen, Xiaojun Quan, Hehong Chen, Ming Yan, Ji Zhang		Paper
Improving Small Language Models' Mathematical Reasoning via Equation-of-Thought Distillation Xunyu Zhu, Jian Li, Yong Liu, Can Ma, Weiping Wang		Paper
Scavenging Hyena: Distilling Transformers into Long Convolution Models Tokiniaina Raharison Ralambomihanta, Shahrad Mohammadzadeh, Mohammad Sami Nur Islam, Wassim Jabbour, Laurence Liang		Paper
DistiLLM: Towards Streamlined Distillation for Large Language Models Jongwoo Ko, Sungnyun Kim, Tianyi Chen, Se-Young Yun		Github Paper
Large Language Model Meets Graph Neural Network in Knowledge Distillation Shengxiang Hu, Guobing Zou, Song Yang, Bofeng Zhang, Yixin Chen		Paper
Unmemorization in Large Language Models via Self-Distillation and Deliberate Imagination Yijiang River Dong, Hongzhou Lin, Mikhail Belkin, Ramon Huerta, Ivan Vulić		Github Paper
Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs Nicolas Boizard, Kevin El-Haddad, Céline Hudelot, Pierre Colombo		Github Github Paper Model
Revisiting Knowledge Distillation for Autoregressive Language Models Qihuang Zhong, Liang Ding, Li Shen, Juhua Liu, Bo Du, Dacheng Tao		Paper
PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning Gyeongman Kim, Doohyuk Jang, Eunho Yang		Paper
Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning Zhaorui Yang, Qian Liu, Tianyu Pang, Han Wang, Haozhe Feng, Minfeng Zhu, Wei Chen		Paper
Wisdom of Committee: Distilling from Foundation Model to Specialized Application Model Zichang Liu, Qingyun Liu, Yuening Li, Liang Liu, Anshumali Shrivastava, Shuchao Bi, Lichan Hong, Ed H. Chi, Zhe Zhao		Paper
Divide-or-Conquer? Which Part Should You Distill Your LLM? Zhuofeng Wu, He Bai, Aonan Zhang, Jiatao Gu, VG Vinod Vydiswaran, Navdeep Jaitly, Yizhe Zhang		Paper
Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and Distillation Phuc Phan, Hieu Tran, Long Phan		Github Paper
Leveraging Zero-Shot Prompting for Efficient Language Model Distillation Lukas Vöge, Vincent Gurgul, Stefan Lessmann		Paper
MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Tasks Letian Peng, Zilong Wang, Feng Yao, Zihan Wang, Jingbo Shang		Github Paper Model
Gecko: Versatile Text Embeddings Distilled from Large Language Models Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren, Blair Chen, Daniel Cer et al		Paper
Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models Taiqiang Wu, Chaofan Tao, Jiahao Wang, Zhe Zhao, Ngai Wong		Paper Blog-Eng Blog-中
Post-Semantic-Thinking: A Robust Strategy to Distill Reasoning Capacity from Large Language Models Xiaoshu Chen, Sihang Zhou, Ke Liang, Xinwang Liu		Paper
Compressing Long Context for Enhancing RAG with AMR-based Concept Distillation Kaize Shi, Xueyao Sun, Qing Li, Guandong Xu		Paper
Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning Yuanhao Yue, Chengyu Wang, Jun Huang, Peng Wang		Paper
RDRec: Rationale Distillation for LLM-based Recommendation Xinfeng Wang, Jin Cui, Yoshimi Suzuki, Fumiyo Fukumoto		Github Paper
LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning Junjie Xu, Zongyu Wu, Minhua Lin, Xiang Zhang, Suhang Wang		Paper
Adversarial Moment-Matching Distillation of Large Language Models Chen Jia		Github Paper
BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation Minchong Li, Feng Zhou, Xiaohui Song		Paper
Multi-Granularity Semantic Revision for Large Language Model Distillation Xiaoyu Liu, Yun Zhang, Wei Li, Simiao Li, Xudong Huang, Hanting Chen, Yehui Tang, Jie Hu, Zhiwei Xiong, Yunhe Wang		Paper
Don't Throw Away Data: Better Sequence Knowledge Distillation Jun Wang, Eleftheria Briakou, Hamid Dadkhahi, Rishabh Agarwal, Colin Cherry, Trevor Cohn		Paper
Key-Point-Driven Mathematical Reasoning Distillation of Large Language Model Xunyu Zhu, Jian Li, Yong Liu, Can Ma, Weiping Wang		Paper
DDK: Distilling Domain Knowledge for Efficient Large Language Models Jiaheng Liu, Chenchen Zhang, Jinyang Guo, Yuanxing Zhang, Haoran Que, Ken Deng, Zhiqi Bai, Jie Liu, Ge Zhang, Jiakai Wang, Yanan Wu, Congnan Liu, Wenbo Su, Jiamang Wang, Lin Qu, Bo Zheng		Paper
Enhancing Data-Limited Graph Neural Networks by Actively Distilling Knowledge from Large Language Models Quan Li, Tianxiang Zhao, Lingwei Chen, Junjie Xu, Suhang Wang		Paper
BOND: Aligning LLMs with Best-of-N Distillation Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Nino Vieillard et al		Paper
LaDiMo: Layer-wise Distillation Inspired MoEfier Sungyoon Kim, Youngjun Kim, Kihyo Moon, Minsung Jang		Paper
Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models Aviv Bick, Kevin Y. Li, Eric P. Xing, J. Zico Kolter, Albert Gu		Paper
Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting Emmanuel Aboah Boateng, Cassiano O. Becker, Nabiha Asghar, Kabir Walia, Ashwin Srinivasan, Ehi Nosakhare, Victor Dibia, Soundar Srinivasan		Paper
Interactive DualChecker for Mitigating Hallucinations in Distilling Large Language Models Meiyun Wang, Masahiro Suzuki, Hiroki Sakaji, Kiyoshi Izumi		Paper
FIRST: Teach A Reliable Large Language Model Through Efficient Trustworthy Distillation KaShun Shum, Minrui Xu, Jianshu Zhang, Zixin Chen, Shizhe Diao, Hanze Dong, Jipeng Zhang, Muhammad Omer Raza		Paper
The Mamba in the Llama: Distilling and Accelerating Hybrid Models Junxiong Wang, Daniele Paliotta, Avner May, Alexander M. Rush, Tri Dao		Github Paper
Efficient Knowledge Distillation: Empowering Small Language Models with Teacher Model Insights Mohamad Ballout, Ulf Krumnack, Gunther Heidemann, Kai-Uwe Kühnberger		Paper
Exploring and Enhancing the Transfer of Distribution in Knowledge Distillation for Autoregressive Language Models Jun Rao, Xuebo Liu, Zepeng Lin, Liang Ding, Jing Li, Dacheng Tao		Paper
LLMR: Knowledge Distillation with a Large Language Model-Induced Reward Dongheng Li, Yongchang Hao, Lili Mou		Github Paper
EchoAtt: Attend, Copy, then Adjust for More Efficient Large Language Models Hossein Rajabzadeh, Aref Jafari, Aman Sharma, Benyamin Jami, Hyock Ju Kwon, Ali Ghodsi, Boxing Chen, Mehdi Rezagholizadeh		Paper
BabyLlama-2: Ensemble-Distilled Models Consistently Outperform Teachers With Limited Data Jean-Loup Tastet, Inar Timiryasov		Paper