Luna: An Evaluation Foundation Model to Catch Language Model Hallucinations with High Accuracy and Low Cost

NLTK: The natural language toolkit

Steven Bird and Edward Loper. 2004 · 2004

Earlier work this paper cites.

Ms marco: A human generated machine reading comprehension dataset

Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016

Earlier work this paper cites.

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

Earlier work this paper cites.

PubMedQA: A dataset for biomedical research question answering

Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. 2019 · 2019

Earlier work this paper cites.

The TechQA dataset

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Earlier work this paper cites.

Retrieval-augmented generation for knowledge-intensive nlp tasks

Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020

Earlier work this paper cites.

COVID-QA: A question answering dataset for COVID-19

Timo Möller, Anthony Reina, Raghavan Jayakumar, and Malte Pietsch. 2020 · 2020

Earlier work this paper cites.

FinQA: A dataset of numerical reasoning over financial data

Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema Moussa, Matt Beane, Ting-Hao Huang, Bryan Routledge, and William Yang Wang. 2021 · 2021

Earlier work this paper cites.

Deberta: Decoding-enhanced bert with disentangled attention

Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2021 · 2021

Earlier work this paper cites.

Cuad: An expert-annotated nlp dataset for legal contract review

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Earlier work this paper cites.

Question answering over electronic devices: A new benchmark dataset and a multi-task learning based QA framework

Abhilash Nandy, Soumya Sharma, Shubham Maddhashiya, Kapil Sachdeva, Pawan Goyal, and NIloy Ganguly. 2021 · 2021

Earlier work this paper cites.

Recipes for building an open-domain chatbot

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Earlier work this paper cites.

Retrieval augmentation reduces hallucination in conversation

Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. 2021 · 2021

Earlier work this paper cites.

Want to reduce labeling cost? GPT-3 can help

Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, and Michael Zeng. 2021 · 2021

Earlier work this paper cites.

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

Earlier work this paper cites.

Hallucinated but factual! inspecting the factuality of hallucinations in abstractive summarization

Meng Cao, Yue Dong, and Jackie Cheung. 2022 · 2022

Earlier work this paper cites.

Diving deep into modes of fact hallucinations in dialogue systems

Souvik Das, Sougata Saha, and Rohini Srihari. 2022 · 2022

Earlier work this paper cites.

On the origin of hallucinations in conversational models: Is it the datasets or the models?

Nouha Dziri, Sivan Milton, Mo Yu, Osmar Zaiane, and Siva Reddy. 2022a · 2022

Earlier work this paper cites.

TRUE: Re-evaluating factual consistency evaluation

Or Honovich, Roee Aharoni, Jonathan Herzig, Hagai Taitelbaum, Doron Kukliansy, Vered Cohen, Thomas Scialom, Idan Szpektor, Avinatan Hassidim, and Yossi Matias. 2022 · 2022

Earlier work this paper cites.

Language models (mostly) know what they know

Original

Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, Scott Johnston, Sheer El-Showk, Andy Jones, Nelson Elhage, Tristan Hume, Anna Chen, Yuntao Bai, Sam Bowman, Stanislav Fort, Deep Ganguli, Danny Hernandez, Josh Jacobson, Jackson Kernion, Shauna Kravec, Liane Lovitt, Kamal Ndousse, Catherine Olsson, Sam Ringer, Dario Amodei, Tom Brown, Jack Clark, Nicholas Joseph, Ben Mann, Sam McCandlish, Chris Olah, and Jared Kaplan. 2022 · 2022

Earlier work this paper cites.

Less annotating, more classifying – addressing the data scarcity issue of supervised machine learning with deep transfer learning and bert - nli

Moritz Laurer, Wouter van Atteveldt, Andreu Casas, and Kasper Welbers. 2022 · 2022

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Factuality enhanced language models for open-ended text generation

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TruthfulQA: Measuring how models mimic human falsehoods

Stephanie Lin, Jacob Hilton, and Owain Evans. 2022 · 2022

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Training language models to follow instructions with human feedback

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022

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Chain-of-thought prompting elicits reasoning in large language models

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. 2022 · 2022

Delucionqa: Detecting hallucinations in domain-specific question answering

Mobashir Sadat, Zhengyu Zhou, Lukas Lange, Jun Araki, Arsalan Gundroo, Bingqing Wang, Rakesh Menon, Md Parvez, and Zhe Feng. 2023 · 2023

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A stitch in time saves nine: Detecting and mitigating hallucinations of llms by validating low-confidence generation

Original

Neeraj Varshney, Wenlin Yao, Hongming Zhang, Jianshu Chen, and Dong Yu. 2023 · 2023

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Freshllms: Refreshing large language models with search engine augmentation

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Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, and Thang Luong. 2023 · 2023

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Ragtruth: A hallucination corpus for developing trustworthy retrieval-augmented language models

Original

Yuanhao Wu, Juno Zhu, Siliang Xu, Kashun Shum, Cheng Niu, Randy Zhong, Juntong Song, and Tong Zhang. 2023 · 2023

Later among the works it cites.