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Attributed text generation via post-hoc research and revision
Original
Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Y Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, et al. 2022 · 2022
Earlier work this paper cites.
Factuality challenges in the era of large language models
Original
Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, Tanmoy Chakraborty, Giovanni Luca Ciampaglia, David Corney, Renee DiResta, Emilio Ferrara, Scott Hale, Alon Halevy, Eduard Hovy, Heng Ji, Filippo Menczer, Ruben Miguez, Preslav Nakov, Dietram Scheufele, Shivam Sharma, and Giovanni Zagni. 2023 · 2023
Earlier work this paper cites.
Factool: Factuality detection in generative AI - A tool augmented framework for multi-task and multi-domain scenarios
Original
I-Chun Chern, Steffi Chern, Shiqi Chen, Weizhe Yuan, Kehua Feng, Chunting Zhou, Junxian He, Graham Neubig, and Pengfei Liu. 2023 · 2023
Earlier work this paper cites.
Chain-of-verification reduces hallucination in large language models
Original
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston. 2023 · 2023
Earlier work this paper cites.

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Fact-checking the output of large language models via token-level uncertainty quantification
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2024

Li, Haonan, Han, Xudong, Wang, Hao et al.

We introduce Loki, an open-source tool designed to address the growing problem of misinformation.

Loki adopts a human-centered approach, striking a balance between the quality of fact-checking and the cost of human involvement.
It decomposes the fact-checking task into a five-step pipeline: breaking down long texts into individual claims, assessing their check-worthiness, generating queries, retrieving evidence, and verifying the claims.
Instead of fully automating the claim verification process, Loki provides essential information at each step to assist human judgment, especially for general users such as journalists and content moderators.

Attributed text generation via post-hoc research and revision
Original
Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Y Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, et al. 2022 · 2022
Earlier work this paper cites.
Factuality challenges in the era of large language models
Original
Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, Tanmoy Chakraborty, Giovanni Luca Ciampaglia, David Corney, Renee DiResta, Emilio Ferrara, Scott Hale, Alon Halevy, Eduard Hovy, Heng Ji, Filippo Menczer, Ruben Miguez, Preslav Nakov, Dietram Scheufele, Shivam Sharma, and Giovanni Zagni. 2023 · 2023
Earlier work this paper cites.
Factool: Factuality detection in generative AI - A tool augmented framework for multi-task and multi-domain scenarios
Original
I-Chun Chern, Steffi Chern, Shiqi Chen, Weizhe Yuan, Kehua Feng, Chunting Zhou, Junxian He, Graham Neubig, and Pengfei Liu. 2023 · 2023
Earlier work this paper cites.
Chain-of-verification reduces hallucination in large language models
Original
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston. 2023 · 2023
Earlier work this paper cites.

Factscore: Fine-grained atomic evaluation of factual precision in long form text generation
Original
Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Wei Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2023 · 2023
Cited alongside, same era.
On the risk of misinformation pollution with large language models
Original
Yikang Pan, Liangming Pan, Wenhu Chen, Preslav Nakov, Min-Yen Kan, and William Yang Wang. 2023 · 2023
Cited alongside, same era.
Factcheck-gpt: End-to-end fine-grained document-level fact-checking and correction of llm output
Original
Yuxia Wang, Revanth Gangi Reddy, Zain Muhammad Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, et al. 2023 · 2023
Cited alongside, same era.
Factcheck-bench: Fine-grained evaluation benchmark for automatic fact-checkers
Original
Yuxia Wang, Revanth Gangi Reddy, Zain Muhammad Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, and Preslav Nakov. 2024a

Fact-checking the output of large language models via token-level uncertainty quantification
Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, and Maxim Panov. 2024 · 2024
Closest in time.
Gpt-4 technical report
Original
OpenAI. 2024 · 2024
Closest in time.
Factuality of large language models in the year 2024
Original
Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, Georgi Georgiev, Rocktim Jyoti Das, and Preslav Nakov. 2024c · 2024
Closest in time.

alphaXiv searches the wider corpus for related work and actual follow-ups.

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Yuxia Wang, Minghan Wang, Hasan Iqbal, Georgi Georgiev, Jiahui Geng, and Preslav Nakov. 2024b

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Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, and Quoc V. Le. 2024a

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Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, and Quoc V. Le. 2024b

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