Papers with fact-verification
TruthTrap: A Bilingual Benchmark for Evaluating Factually Correct Yet Misleading Information in Question Answering (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) are increasingly used to answer factual, information-seeking questions (ISQs). |
| Approach: | They propose to use a dataset to evaluate large language models to generate human-like text on ISQs in two languages, English and Farsi, and then use it to evaluate nine LLMs. |
| Outcome: | The proposed dataset shows that accuracy drops by 25% when models encounter misleading yet factual hints. |
Factual Confidence of LLMs: on Reliability and Robustness of Current Estimators (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) tend to be unreliable on fact-based answers. |
| Approach: | They propose a framework for comparing LLMs' confidence over fact-based answers with hidden-state probes that are more reliable than hidden-status probes. |
| Outcome: | The proposed methods show that hidden-state probes provide the most reliable confidence estimates despite requiring access to weights and supervision data. |
Constrained Fact Verification for FEVER (2020.emnlp-main)
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| Challenge: | Existing methods for fact verification rely on extracted evidence, but there is little work on understanding the reasoning process. |
| Approach: | They propose a method that enforces a closed-world reliance on extracted evidence to verify a claim's factuality. |
| Outcome: | The proposed model outperforms existing models on the FEVER shared task and shows that it is more accurate than previous models. |