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A Fast Post-Training Pruning Framework for Transformers

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Woosuk Kwon, Sehoon Kim, Michael W. Mahoney, Joseph Hassoun, Kurt Keutzer, and Amir Gholami. 2022 · 2022

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Adapting Language Models to Compress Contexts

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Alexis Chevalier, Alexander Wettig, Anirudh Ajith, and Danqi Chen. 2023 · 2023

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SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

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Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, and Dan Alistarh. 2023 · 2023

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A Survey on In-context Learning

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Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, Lei Li, and Zhifang Sui. 2023 · 2023

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Extending Context Window of Large Language Models via Semantic Compression

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Weizhi Fei, Xueyan Niu, Pingyi Zhou, Lu Hou, Bo Bai, Lei Deng, and Wei Han. 2023 · 2023

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GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

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Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. 2023 · 2023

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LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models

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Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, and Lili Qiu. 2023 · 2023

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Efficient Memory Management for Large Language Model Serving with PagedAttention

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Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. 2023 · 2023

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Compressing Context to Enhance Inference Efficiency of Large Language Models

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Yucheng Li, Bo Dong, Chenghua Lin, and Frank Guerin. 2023 · 2023

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Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models

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Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao. 2023 · 2023

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NexusRaven: a commercially-permissive Language Model for function calling. In NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following

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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

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Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023 · 2023

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RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation

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Fangyuan Xu, Weijia Shi, and Eunsol Choi. 2023 · 2023

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MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action

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Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Ehsan Azarnasab, Faisal Ahmed, Zicheng Liu, Ce Liu, Michael Zeng, and Lijuan Wang. 2023 · 2023

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Retrieval Augmented Generation (RAG)

LangChain Docs. 2024 · 2024

Closest in time.

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

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Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang, Chuang Gan, and Song Han. 2024 · 2024

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APAR: LLMs Can Do Auto-Parallel Auto-Regressive Decoding

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Mingdao Liu, Aohan Zeng, Bowen Wang, Peng Zhang, Jie Tang, and Yuxiao Dong. 2024 · 2024

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TOFU: A Task of Fictitious Unlearning for LLMs

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Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C. Lipton, and J. Zico Kolter. 2024 · 2024

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OpenEQA: Embodied Question Answering in the Era of Foundation Models. In Conference on Computer Vision and Pattern Recognition (CVPR)

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