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Large Language Models (LLMs) have shown their success in language understanding and reasoning on general topics.
Causation, prediction, and search
Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman · 2000
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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A linear non-gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, and Antti Kerminen · 2006
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Causal mediation analysis
Raymond Hicks and Dustin Tingley · 2011
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Dynamic treatment regimes
Bibhas Chakraborty and Susan A Murphy · 2014
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Lingam: Non-gaussian methods for estimating causal structures
Shohei Shimizu · 2014
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Event2mind: Commonsense inference on events, intents, and reactions
Hannah Rashkin, Maarten Sap, Emily Allaway, Noah A Smith, and Yejin Choi · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Dags with no tears: Continuous optimization for structure learning
Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing · 2018
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Review of causal discovery methods based on graphical models
Clark Glymour, Kun Zhang, and Peter Spirtes · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Joint reasoning for temporal and causal relations
Qiang Ning, Zhili Feng, Hao Wu, and Dan Roth · 2019
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Dag-gnn: Dag structure learning with graph neural networks
Yue Yu, Jie Chen, Tian Gao, and Mo Yu · 2019
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Anoce: Analysis of causal effects with multiple mediators via constrained structural learning
Hengrui Cai, Rui Song, and Wenbin Lu · 2020
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Causalml: Python package for causal machine learning
Huigang Chen, Totte Harinen, Jeong-Yoon Lee, Mike Yung, and Zhenyu Zhao · 2020
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Exploiting cloze questions for few shot text classification and natural language inference
Timo Schick and Hinrich Schütze · 2020
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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh · 2020
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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Template-based named entity recognition using bart
Leyang Cui, Yu Wu, Jian Liu, Sen Yang, and Yue Zhang · 2021
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Retrieval-augmented generation for large language models: A survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang · 2023
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Learned causal method prediction
Shantanu Gupta, Cheng Zhang, and Agrin Hilmkil · 2023
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Chatgpt for good? on opportunities and challenges of large language models for education
Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan Günnemann, Eyke Hüllermeier, et al · 2023
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Causal reasoning and large language models: Opening a new frontier for causality
Emre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan · 2023
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Scaling down to scale up: A guide to parameter-efficient fine-tuning
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Adi Haviv, Jonathan Berant, and Amir Globerson · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Internet-augmented dialogue generation
Mojtaba Komeili, Kurt Shuster, and Jason Weston · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Are nlp models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal · 2021
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Cross-domain reasoning via template filling
Dheeraj Rajagopal, Vivek Khetan, Bogdan Sacaleanu, Anatole Gershman, Andrew Fano, and Eduard Hovy · 2021
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A survey on causal inference
Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, and Aidong Zhang · 2021
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Vladislav Lialin, Vijeta Deshpande, and Anna Rumshisky · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
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Can large language models build causal graphs?
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Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
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Causaldm, 2023
Rui Song, Hengrui Cai, Yang Xu, Runzhe Wan, and Lin Ge · 2023
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Cola: contextualized commonsense causal reasoning from the causal inference perspective
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Gpt4tools: Teaching large language model to use tools via self-instruction
Rui Yang, Lin Song, Yanwei Li, Sijie Zhao, Yixiao Ge, Xiu Li, and Ying Shan · 2023
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A survey on causal reinforcement learning, 2023
Yan Zeng, Ruichu Cai, Fuchun Sun, Libo Huang, and Zhifeng Hao · 2023
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Understanding causality with large language models: Feasibility and opportunities
Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao, Wenbo Gong, Agrin Hilmkil, Joel Jennings, Chao Ma, Tom Minka, Nick Pawlowski, et al · 2023
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Graph-toolformer: To empower llms with graph reasoning ability via prompt augmented by chatgpt
Jiawei Zhang · 2023
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Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, and Kun Zhang · 2023
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Benchmarking large language models in retrieval-augmented generation
Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun · 2024
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