VERITAS: A Unified Approach to Reliability Evaluation

Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs

Original

Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 1903

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Multi-news: a large-scale multi-document summarization dataset and abstractive hierarchical model

Original

Alexander R. Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir R. Radev. 2019 · 1906

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BIGPATENT: A large-scale dataset for abstractive and coherent summarization

Original

Eva Sharma, Chen Li, and Lu Wang. 2019 · 1906

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Pubmedqa: A dataset for biomedical research question answering

Original

Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W. Cohen, and Xinghua Lu. 2019 · 1909

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Billsum: A corpus for automatic summarization of US legislation

Original

Anastassia Kornilova and Vlad Eidelman. 2019 · 1910

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Samsum corpus: A human-annotated dialogue dataset for abstractive summarization

Original

Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019 · 1911

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Realm: Retrieval-augmented language model pre-training

Original

Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2002

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Language models are few-shot learners

Original

Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2005

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Newsqa: A machine comprehension dataset

Original

Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2016 · 2016

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Searchqa: A new q&a dataset augmented with context from a search engine

Original

Matthew Dunn, Levent Sagun, Mike Higgins, V Ugur Guney, Volkan Cirik, and Kyunghyun Cho. 2017 · 2017

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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension

Original

Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017 · 2017

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Are you smarter than a sixth grader? textbook question answering for multimodal machine comprehension

Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017

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Race: Large-scale reading comprehension dataset from examinations

Original

Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017

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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

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

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

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Adversarial NLI: A new benchmark for natural language understanding

Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020 · 2020

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KILT: a benchmark for knowledge intensive language tasks

Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, Vassilis Plachouras, Tim Rocktäschel, and Sebastian Riedel. 2021 · 2021

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Exam: How to evaluate retrieve-and-generate systems for users who do not (yet) know what they want

David P Sander and Laura Dietz. 2021 · 2021

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Retrieval augmentation reduces hallucination in conversation

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

Cited alongside, same era.

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

Cited alongside, same era.

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

Cited alongside, same era.

TruthfulQA: Measuring how models mimic human falsehoods

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

Cited alongside, same era.

Prediction-powered inference

Do language models know when they’re hallucinating references?

Ayush Agrawal, Mirac Suzgun, Lester Mackey, and Adam Kalai. 2024 · 2024

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Luna: An evaluation foundation model to catch language model hallucinations with high accuracy and low cost

Original

Masha Belyi, Robert Friel, Shuai Shao, and Atindriyo Sanyal. 2024 · 2024

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Scaling instruction-finetuned language models

Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2024 · 2024

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Qlora: Efficient finetuning of quantized llms

Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2024 · 2024

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Does fine-tuning llms on new knowledge encourage hallucinations?

Original

Zorik Gekhman, Gal Yona, Roee Aharoni, Matan Eyal, Amir Feder, Roi Reichart, and Jonathan Herzig. 2024 · 2024

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