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Although large language models (LLMs) store vast amount of knowledge in their parameters, they still have limitations in the memorization and utilization of certain knowledge, leading to undesired behaviors such as generating untruthful and inaccurate responses.
Jec-qa: A legal-domain question answering dataset
Haoxiang Zhong, Chaojun Xiao, Cunchao Tu, T. Zhang, Zhiyuan Liu, and Maosong Sun. 2019 · 1911
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Review of "algorithmic learning in a random world by vovk, gammerman and shafer", springer, 2005, ISBN: 0-387-00152-2
James Law. 2006 · 2005
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Aligning language models to explicitly handle ambiguity
Hyuhng Joon Kim, Youna Kim, Cheonbok Park, Junyeob Kim, Choonghyun Park, Kang Min Yoo, Sang-goo Lee, and Taeuk Kim. 2024a · 2007
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
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Pubmedqa: A dataset for biomedical research question answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W. Cohen, and Xinghua Lu. 2019 · 2019
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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019 · 2019
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Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020 · 2020
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Building and evaluating open-domain dialogue corpora with clarifying questions
Mohammad Aliannejadi, Julia Kiseleva, Aleksandr Chuklin, Jeff Dalton, and Mikhail Burtsev. 2021 · 2021
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Finqa: A dataset of numerical reasoning over financial data
Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema N Moussa, Matthew I. Beane, Ting-Hao ’Kenneth’ Huang, Bryan R. Routledge, and William Yang Wang. 2021 · 2021
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Measuring and improving consistency in pretrained language models
Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, and Yoav Goldberg. 2021 · 2021
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Abg-coqa: Clarifying ambiguity in conversational question answering
Meiqi Guo, Mingda Zhang, Siva Reddy, and Malihe Alikhani. 2021 · 2021
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Tat-qa: A question answering benchmark on a hybrid of tabular and textual content in finance
Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat seng Chua. 2021 · 2021
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TopiOCQA: Open-domain conversational question answering with topic switching
Vaibhav Adlakha, Shehzaad Dhuliawala, Kaheer Suleman, Harm de Vries, and Siva Reddy. 2022 · 2022
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Rlprompt: Optimizing discrete text prompts with reinforcement learning
Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric Xing, and Zhiting Hu. 2022 · 2022
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Calibrating factual knowledge in pretrained language models
Qingxiu Dong, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei Li. 2022 · 2022
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Maieutic prompting: Logically consistent reasoning with recursive explanations
Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, and Yejin Choi. 2022 · 2022
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Language models (mostly) know what they know
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al. 2022 · 2022
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Clam: Selective clarification for ambiguous questions with large language models
Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. 2022 · 2022
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Teaching models to express their uncertainty in words
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022a · 2022
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What makes good in-context examples for gpt-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, William B Dolan, Lawrence Carin, and Weizhu Chen. 2022a · 2022
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022 · 2022
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Memory-assisted prompt editing to improve GPT-3 after deployment
Aman Madaan, Niket Tandon, Peter Clark, and Yiming Yang. 2022 · 2022
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Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 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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Reducing conversational agents’ overconfidence through linguistic calibration
Sabrina J Mielke, Arthur Szlam, Emily Dinan, and Y-Lan Boureau. 2022 · 2022
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Memory-based model editing at scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D. Manning, and Chelsea Finn. 2022 · 2022
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Entity cloze by date: What lms know about unseen entities
Yasumasa Onoe, Michael J. Q. Zhang, Eunsol Choi, and Greg Durrett. 2022 · 2022
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Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2022 · 2022
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MuSiQue: Multihop questions via single-hop question composition
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022 · 2022
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Iteratively prompt pre-trained language models for chain of thought
Boshi Wang, Xiang Deng, and Huan Sun. 2022 · 2022
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Can NLP models ’identify’, ’distinguish’, and ’justify’ questions that don’t have a definitive answer?
Ayushi Agarwal, Nisarg Patel, Neeraj Varshney, Mihir Parmar, Pavan Mallina, Aryan Bhavin Shah, Srihari Raju Sangaraju, Tirth Patel, Nihar Thakkar, and Chitta Baral. 2023 · 2023
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The internal state of an LLM knows when it’s lying
Amos Azaria and Tom M. Mitchell. 2023 · 2023
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Discovering latent knowledge in language models without supervision
Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. 2023 · 2023
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A survey on proactive dialogue systems: Problems, methods, and prospects
Yang Deng, Wenqiang Lei, Wai Lam, and Tat-Seng Chua. 2023a · 2023
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Prompting and evaluating large language models for proactive dialogues: Clarification, target-guided, and non-collaboration
Yang Deng, Lizi Liao, Liang Chen, Hongru Wang, Wenqiang Lei, and Tat-Seng Chua. 2023b · 2023
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Black-box prompt learning for pre-trained language models
Shizhe Diao, Zhichao Huang, Ruijia Xu, Xuechun Li, LIN Yong, Xiao Zhou, and Tong Zhang. 2023 · 2023
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Shifting attention to relevance: Towards the uncertainty estimation of large language models
Jinhao Duan, Hao Cheng, Shiqi Wang, Chenan Wang, Alex Zavalny, Renjing Xu, Bhavya Kailkhura, and Kaidi Xu. 2023 · 2023
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Improving language model negotiation with self-play and in-context learning from ai feedback
Yao Fu, Hao Peng, Tushar Khot, and Mirella Lapata. 2023 · 2023
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Precise zero-shot dense retrieval without relevance labels
Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan. 2023 · 2023
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Aging with GRACE: lifelong model editing with discrete key-value adaptors
Tom Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, and Marzyeh Ghassemi. 2023 · 2023
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Uncertainty in natural language processing: Sources, quantification, and applications
Mengting Hu, Zhen Zhang, Shiwan Zhao, Minlie Huang, and Bingzhe Wu. 2023 · 2023
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Large language models can self-improve
Jiaxin Huang, Shixiang Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han. 2023a · 2023
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Transformer-patcher: One mistake worth one neuron
Zeyu Huang, Yikang Shen, Xiaofeng Zhang, Jie Zhou, Wenge Rong, and Zhang Xiong. 2023d · 2023
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In-context demonstration selection with cross entropy difference
Dan Iter, Reid Pryzant, Ruochen Xu, Shuohang Wang, Yang Liu, Yichong Xu, and Chenguang Zhu. 2023 · 2023
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Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
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Calibrating language models via augmented prompt ensembles
Mingjian Jiang, Yangjun Ruan, Sicong Huang, Saifei Liao, Silviu Pitis, Roger Baker Grosse, and Jimmy Ba. 2023a · 2023
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Active retrieval augmented generation
Zhengbao Jiang, Frank Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023b · 2023
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Realtime QA: what’s the answer right now?
Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, Ronan Le Bras, Akari Asai, Xinyan Yu, Dragomir Radev, Noah A. Smith, Yejin Choi, and Kentaro Inui. 2023 · 2023
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Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation
Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. 2023 · 2023
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Conformal prediction with large language models for multi-choice question answering
Bhawesh Kumar, Charlie Lu, Gauri Gupta, Anil Palepu, David R. Bellamy, Ramesh Raskar, and Andrew Beam. 2023 · 2023
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Robust prompt optimization for large language models against distribution shifts
Moxin Li, Wenjie Wang, Fuli Feng, Yixin Cao, Jizhi Zhang, and Tat-Seng Chua. 2023b · 2023
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Finding support examples for in-context learning
Xiaonan Li and Xipeng Qiu. 2023 · 2023
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We’re afraid language models aren’t modeling ambiguity
Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah A. Smith, and Yejin Choi. 2023 · 2023
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Dr.ICL: Demonstration-retrieved in-context learning
Man Luo, Xin Xu, Zhuyun Dai, Panupong Pasupat, Mehran Kazemi, Chitta Baral, Vaiva Imbrasaite, and Vincent Y Zhao. 2023 · 2023
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Query rewriting in retrieval-augmented large language models
Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan. 2023 · 2023
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Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
Potsawee Manakul, Adian Liusie, and Mark Gales. 2023 · 2023
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Samuel Marks and Max Tegmark. 2023 · 2023
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Which examples to annotate for in-context learning? towards effective and efficient selection
Costas Mavromatis, Balasubramaniam Srinivasan, Zhengyuan Shen, Jiani Zhang, Huzefa Rangwala, Christos Faloutsos, and George Karypis. 2023 · 2023
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Mass-editing memory in a transformer
Kevin Meng, Arnab Sen Sharma, Alex J. Andonian, Yonatan Belinkov, and David Bau. 2023 · 2023
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Factscore: Fine-grained atomic evaluation of factual precision in long form text generation
Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen tau Yih, Pang Wei Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2023 · 2023
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Clarifygpt: Empowering llm-based code generation with intention clarification
Fangwen Mu, Lin Shi, Song Wang, Zhuohao Yu, Binquan Zhang, ChenXue Wang, Shichao Liu, and Qing Wang. 2023 · 2023
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Bridging the preference gap between retrievers and LLMs
Zixuan Ke, Weize Kong, Cheng Li, Mingyang Zhang, Qiaozhu Mei, and Michael Bendersky. 2024 · 2024
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Adaptive contrastive decoding in retrieval-augmented generation for handling noisy contexts
Youna Kim, Hyuhng Joon Kim, Cheonbok Park, Choonghyun Park, Hyunsoo Cho, Junyeob Kim, Kang Min Yoo, Sang-goo Lee, and Taeuk Kim. 2024b · 2024
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Semantic entropy probes: Robust and cheap hallucination detection in llms
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To clarify or not to clarify: A comparative analysis of clarification classification with fine-tuning, prompt tuning, and prompt engineering
Alina Leippert, Tatiana Anikina, Bernd Kiefer, and Josef Genabith. 2024 · 2024
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Think twice before trusting: Self-detection for large language models through comprehensive answer reflection
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Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. 2023 · 2023
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On the risk of misinformation pollution with large language models
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Grips: Gradient-free, edit-based instruction search for prompting large language models
Archiki Prasad, Peter Hase, Xiang Zhou, and Mohit Bansal. 2023 · 2023
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Measuring and narrowing the compositionality gap in language models
Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis. 2023 · 2023
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Automatic prompt optimization with “gradient descent” and beam search
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Language models are greedy reasoners: A systematic formal analysis of chain-of-thought
Abulhair Saparov and He He. 2023 · 2023
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Prompt optimization via adversarial in-context learning
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