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Large Language Models (LLMs) are becoming vital tools that help us solve and understand complex problems by acting as digital assistants.
Rhetorical structure theory: Toward a functional theory of text organization
William C Mann and Sandra A Thompson · 1988
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Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems
Zana Buçinca, Phoebe Lin, Krzysztof Z. Gajos, and Elena L. Glassman · 2001
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ConceptNet—a practical commonsense reasoning tool-kit
Hugo Liu and Push Singh · 2004
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Gagan Bansal, Tongshuang Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel S. Weld · 2006
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning · 2015
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant · 2019
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Evaluating explainable AI: Which algorithmic explanations help users predict model behavior?
Peter Hase and Mohit Bansal · 2020
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Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making
Yunfeng Zhang, Q. Vera Liao, and Rachel K. E. Bellamy · 2020
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Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al · 2021
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Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making
Xinru Wang and Ming Yin · 2021
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Explainability pitfalls: Beyond dark patterns in explainable ai
Upol Ehsan and Mark O Riedl · 2021
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Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
Alon Jacovi, Ana Marasović, Tim Miller, and Yoav Goldberg · 2021
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SemEval-2021 task 6: Detection of persuasion techniques in texts and images
Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, and Giovanni Da San Martino · 2021
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Explanations for CommonsenseQA: New Dataset and Models
Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, and Dinesh Garg · 2021
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Manipulating and Measuring Model Interpretability, August 2021
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, and Hanna Wallach · 2021
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Reframing human-AI collaboration for generating free-text explanations
Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark Riedl, and Yejin Choi · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Machine explanations and human understanding
Chacha Chen, Shi Feng, Amit Sharma, and Chenhao Tan · 2022
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HIVE: Evaluating the Human Interpretability of Visual Explanations, July 2022
Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, and Olga Russakovsky · 2022
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The unreliability of explanations in few-shot prompting for textual reasoning
Xi Ye and Greg Durrett · 2022
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Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned
Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, et al · 2022
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Introducing ChatGPT, 2022
OpenAI · 2022
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Clear: Generative counterfactual explanations on graphs
Jing Ma, Ruocheng Guo, Saumitra Mishra, Aidong Zhang, and Jundong Li · 2022
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MuSR: Testing the limits of chain-of-thought with multistep soft reasoning
Zayne Sprague, Xi Ye, Kaj Bostrom, Swarat Chaudhuri, and Greg Durrett · 2023
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Situated natural language explanations
Zining Zhu, Haoming Jiang, Jingfeng Yang, Sreyashi Nag, Chao Zhang, Jie Huang, Yifan Gao, Frank Rudzicz, and Bing Yin · 2023
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Faithful Chain-of-Thought Reasoning
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch · 2023
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Graph of thoughts: Solving elaborate problems with large language models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, et al · 2023
Survey of Hallucination in Natural Language Generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung · 2023
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Towards Understanding Sycophancy in Language Models, October 2023
Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud, Amanda Askell, Samuel R. Bowman, Newton Cheng, Esin Durmus, Zac Hatfield-Dodds, Scott R. Johnston, Shauna Kravec, Timothy Maxwell, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, and Ethan Perez · 2023
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Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support, March 2023
Tim Miller · 2023
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Solving olympiad geometry without human demonstrations
Trieu H Trinh, Yuhuai Wu, Quoc V Le, He He, and Thang Luong · 2024
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Presentations by the humans and for the humans: Harnessing LLMs for generating persona-aware slides from documents
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Everything of thoughts: Defying the law of penrose triangle for thought generation
Ruomeng Ding, Chaoyun Zhang, Lu Wang, Yong Xu, Minghua Ma, Wei Zhang, Si Qin, Saravan Rajmohan, Qingwei Lin, and Dongmei Zhang · 2023
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Are machine rationales (not) useful to humans? measuring and improving human utility of free-text rationales
Brihi Joshi, Ziyi Liu, Sahana Ramnath, Aaron Chan, Zhewei Tong, Shaoliang Nie, Qifan Wang, Yejin Choi, and Xiang Ren · 2023
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Is explanation the cure? misinformation mitigation in the short term and long term
Yi-Li Hsu, Shih-Chieh Dai, Aiping Xiong, and Lun-Wei Ku · 2023
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Using natural language explanations to rescale human judgments
Manya Wadhwa, Jifan Chen, Junyi Jessy Li, and Greg Durrett · 2023
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Evaluating the utility of model explanations for model development
Shawn Im, Jacob Andreas, and Yilun Zhou · 2023
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Are large language models post hoc explainers?
Nicholas Kroeger, Dan Ley, Satyapriya Krishna, Chirag Agarwal, and Himabindu Lakkaraju · 2023
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Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, J Zico Kolter, and Matt Fredrikson · 2023
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Ishani Mondal, Shwetha S, Anandhavelu Natarajan, Aparna Garimella, Sambaran Bandyopadhyay, and Jordan Boyd-Graber · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan · 2024
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Large Language Models Help Humans Verify Truthfulness – Except When They Are Convincingly Wrong
Chenglei Si, Navita Goyal, Sherry Tongshuang Wu, Chen Zhao, Shi Feng, Hal Daumé III, and Jordan Boyd-Graber · 2024
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Can language models teach? teacher explanations improve student performance via personalization
Swarnadeep Saha, Peter Hase, and Mohit Bansal · 2024
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Properties and challenges of llm-generated explanations
Jenny Kunz and Marco Kuhlmann · 2024
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Language models don’t always say what they think: unfaithful explanations in chain-of-thought prompting
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Yi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang, Ruoxi Jia, and Weiyan Shi · 2024
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Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al · 2024
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Evaluating large language models in theory of mind tasks, 2024
Michal Kosinski · 2024
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Faith and fate: Limits of transformers on compositionality
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Why think step by step? Reasoning emerges from the locality of experience
Ben Prystawski, Michael Li, and Noah Goodman · 2024
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Robust stochastic graph generator for counterfactual explanations
Mario Alfonso Prado-Romero, Bardh Prenkaj, and Giovanni Stilo · 2024
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Faithfulness vs. plausibility: On the (un)reliability of explanations from large language models, 2024
Chirag Agarwal, Sree Harsha Tanneru, and Himabindu Lakkaraju · 2024
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What does the Knowledge Neuron Thesis Have to do with Knowledge?
Jingcheng Niu, Andrew Liu, Zining Zhu, and Gerald Penn · 2024
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REV: Information-theoretic evaluation of free-text rationales
Hanjie Chen, Faeze Brahman, Xiang Ren, Yangfeng Ji, Yejin Choi, and Swabha Swayamdipta · 2030
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