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Large Language Models (LLMs) have shown exceptional abilities for multiple different natural language processing tasks.
Prediction and entropy of printed english
Claude E. Shannon. 1951 · 1951
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Kuttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2005
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Fine-grained named entity typing over distantly supervised data based on refined representations
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A survey on knowledge graphs: Representation, acquisition, and applications
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Constrained iterative labeling for open information extraction
Keshav Kolluru, Vaibhav Adlakha, Samarth Aggarwal, Mausam, and Soumen Chakrabarti. 2020 · 2020
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Training verifiers to solve math word problems
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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A survey on in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui. 2022 · 2022
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Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022 · 2022
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Chain of thought prompting elicits reasoning in large language models
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Prompt compression and contrastive conditioning for controllability and toxicity reduction in language models
David Wingate, Mohammad Shoeybi, and Taylor Sorensen. 2022 · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Lost in the middle: How language models use long contexts
Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023 · 2023
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Reasoning on graphs: Faithful and interpretable large language model reasoning
Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, and Shirui Pan. 2023 · 2023
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Unifying large language models and knowledge graphs: A roadmap
Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. 2023 · 2023
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Generative agents: Interactive simulacra of human behavior
Joon Sung Park, Joseph C. O’Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. 2023 · 2023
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Discrete prompt compression with reinforcement learning
Hoyoun Jung and Kyung-Joong Kim. 2023 · 2023
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Kg-gpt: A general framework for reasoning on knowledge graphs using large language models
Jiho Kim, Yeonsu Kwon, Yohan Jo, and Edward Choi. 2023 · 2023
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Yucheng Li. 2023 · 2023
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Llmlingua: Compressing prompts for accelerated inference of large language models
Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, and Lili Qiu. 2023a
Cited in the paper.
Longllmlingua: Accelerating and enhancing llms in long context scenarios via prompt compression
Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, and Lili Qiu. 2023b
Cited in the paper.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
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Zhaozhuo Xu, Zirui Liu, Beidi Chen, Yuxin Tang, Jue Wang, Kaixiong Zhou, Xia Hu, and Anshumali Shrivastava. 2023 · 2023
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Understanding in-context learning from repetitions
Jianhao Yan, Jin Xu, Chiyu Song, Chenming Wu, Yafu Li, and Yue Zhang. 2023 · 2023
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A systematic survey of prompt engineering in large language models: Techniques and applications
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Sohel Mondal, and Aman Chadha. 2024 · 2024
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