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In this paper, we comprehensively investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation and its subsequent impact on information-intensive applications, particularly Open-Domain Question Answering (ODQA) systems.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 1910
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HuggingFace’s Transformers: State-of-the-art Natural Language Processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 1910
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Language Models are Few-Shot Learners
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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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. 2021 · 2005
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Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Gautier Izacard and Edouard Grave. 2021 · 2007
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The Probabilistic Relevance Framework: BM25 and Beyond
Stephen Robertson and Hugo Zaragoza. 2009 · 2009
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Is Wikipedia Biased?
Shane Greenstein and Feng Zhu. 2012 · 2012
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Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes
Srijan Kumar, Robert West, and Jure Leskovec. 2016 · 2016
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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, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
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Defending against neural fake news
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019 · 2019
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Retrieval Augmented Language Model Pre-Training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020 · 2020
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Dense Passage Retrieval for Open-Domain Question Answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
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Truth, lies, and automation: How language models could change disinformation
Ben Buchanan, Andrew Lohn, and Micah Musser. 2021 · 2021
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All that’s ’human’ is not gold: Evaluating human evaluation of generated text
Elizabeth Clark, Tal August, Sofia Serrano, Nikita Haduong, Suchin Gururangan, and Noah A. Smith. 2021 · 2021
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SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval
Thibault Formal, Carlos Lassance, Benjamin Piwowarski, and Stéphane Clinchant. 2021 · 2021
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Infosurgeon: Cross-media fine-grained information consistency checking for fake news detection
Yi R. Fung, Christopher Thomas, Revanth Gangi Reddy, Sandeep Polisetty, Heng Ji, Shih-Fu Chang, Kathleen R. McKeown, Mohit Bansal, and Avi Sil. 2021 · 2021
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NELA-GT-2021: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles
Maurício Gruppi, Benjamin D. Horne, and Sibel Adalı. 2022 · 2021
Cited alongside, same era.
Pyserini: A Python Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations
Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, and Rodrigo Nogueira. 2021 · 2021
Cited alongside, same era.
What’s in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus
Answering Open-Domain Multi-Answer Questions via a Recall-then-Verify Framework
Zhihong Shao and Minlie Huang. 2022 · 2022
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Detecting computer-generated disinformation
Harald Stiff and Fredrik Johansson. 2022 · 2022
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Context Generation Improves Open Domain Question Answering
Dan Su, Mostofa Patwary, Shrimai Prabhumoye, Peng Xu, Ryan Prenger, Mohammad Shoeybi, Pascale Fung, Anima Anandkumar, and Bryan Catanzaro. 2022 · 2022
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Defending Against Poisoning Attacks in Open-Domain Question Answering
Orion Weller, Aleem Khan, Nathaniel Weir, Dawn Lawrie, and Benjamin Van Durme. 2022 · 2022
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Generate rather than Retrieve: Large Language Models are Strong Context Generators
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Alexandra Luccioni and Joseph Viviano. 2021 · 2021
Cited alongside, same era.
ContraQA: Question Answering under Contradicting Contexts
Liangming Pan, Wenhu Chen, Min-Yen Kan, and William Yang Wang. 2021 · 2021
Cited alongside, same era.
Hung-Ting Chen, Michael J. Q. Zhang, and Eunsol Choi. 2022 · 2022
Cited alongside, same era.
FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation
Sebastian Hofstätter, Jiecao Chen, Karthik Raman, and Hamed Zamani. 2022 · 2022
Cited alongside, same era.
Faking fake news for real fake news detection: Propaganda-loaded training data generation
Kung-Hsiang Huang, Kathleen R. McKeown, Preslav Nakov, Yejin Choi, and Heng Ji. 2022 · 2022
Cited alongside, same era.
Survey of Hallucination in Natural Language Generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Wenliang Dai, Andrea Madotto, and Pascale Fung. 2022 · 2022
Cited alongside, same era.
All the news that’s fit to fabricate: Ai-generated text as a tool of media misinformation
Sarah Kreps, R Miles McCain, and Miles Brundage. 2022 · 2022
Cited alongside, same era.
StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models
Adam Liška, Tomáš Kočiský, Elena Gribovskaya, Tayfun Terzi, Eren Sezener, Devang Agrawal, Cyprien de Masson d’Autume, Tim Scholtes, Manzil Zaheer, Susannah Young, Ellen Gilsenan-McMahon, Sophia Austin, Phil Blunsom, and Angeliki Lazaridou. 2022 · 2022
Cited alongside, same era.
Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng, and Meng Jiang. 2022 · 2022
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Gpt-wiki-intro (revision 0e458f5)
Aaditya Bhat. 2023 · 2023
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Poisoning Web-Scale Training Datasets is Practical
Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka, Will Pearce, Hyrum Anderson, Andreas Terzis, Kurt Thomas, and Florian Tramèr. 2023 · 2023
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On the Possibilities of AI-Generated Text Detection
Souradip Chakraborty, Amrit Singh Bedi, Sicheng Zhu, Bang An, Dinesh Manocha, and Furong Huang. 2023 · 2023
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Josh A. Goldstein, Girish Sastry, Micah Musser, Renee DiResta, Matthew Gentzel, and Katerina Sedova. 2023 · 2023
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Giwon Hong, Jeonghwan Kim, Junmo Kang, Sung-Hyon Myaeng, and Joyce Jiyoung Whang. 2023 · 2023
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DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D. Manning, and Chelsea Finn. 2023 · 2023
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OpenAI. 2023 · 2023
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Who Funds Misinformation? A Systematic Analysis of the Ad-related Profit Routines of Fake News sites
Emmanouil Papadogiannakis, Panagiotis Papadopoulos, Evangelos P. Markatos, and Nicolas Kourtellis. 2023 · 2023
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Can AI-Generated Text be Reliably Detected?
Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi. 2023 · 2023
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