Papers with factuality of LLMs
OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs (2025.coling-main)
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Yuxia Wang, Minghan Wang, Hasan Iqbal, Georgi N. Georgiev, Jiahui Geng, Iryna Gurevych, Preslav Nakov
| Challenge: | Large language models (LLMs) generate naturallysounding answers over a broad range of human inquiries, but they still produce content that deviates from real-world facts. |
| Approach: | They propose a framework for building customized automatic fact-checking systems, benchmarking their accuracy, evaluating factuality of LLMs, and verifying claims in a document. |
| Outcome: | The proposed framework assesses the factuality of free-form responses in open domains and evaluates factually of LLMs. |
Factuality of Large Language Models: A Survey (2024.emnlp-main)
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Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, Fei Liu, Georgi Georgiev, Rocktim Das, Preslav Nakov
| Challenge: | Large language models (LLMs) are factually incorrect, which limits their applicability in real-world scenarios. |
| Approach: | They analyze existing work to identify major challenges and their associated causes . they propose to evaluate LLMs using a variety of measures to mitigate factual errors . |
| Outcome: | The proposed methods are based on a variety of datasets and proposed strategies to mitigate factual errors. |