Localizing and Mitigating Errors in Long-form Question Answering (2025.findings-acl)
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| Challenge: | Long-form question answering (LFQA) answers are prone to hallucinations and factual inconsistencies, challenging their faithful evaluation. |
| Approach: | They propose a dataset with localized error annotations for human-written and model-generated LFQA answers. |
| Outcome: | The proposed approach reduces errors and improves quality of the answers across multiple models. |
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| Challenge: | Long-form question answering (LFQA) generates a paragraph-length answer for a given question. |
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| Challenge: | Long-form question answering (LFQA) involves retrieving documents relevant to a given question and using them to generate a paragraph-length answer. |
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| Challenge: | Long-form question answering (LFQA) is an emerging research area within QA . however, its flexibility poses enormous challenges for evaluation . |
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| Challenge: | Recent advances in large language models have improved summarization, but they still face a challenge of hallucination. |
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| Challenge: | Exemplification is a process by which writers explain or clarify a concept by providing an example. |
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| Challenge: | Question Answering (QA) with large language models has shown impressive performance, yet hallucinations persist . incorporating LQC into QA pipelines reduces hallucinosities while preserving strong answer quality. |
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| Challenge: | a comprehensive and fine-grained measurement of the hallucination is crucial for LLMs' wide applications. |
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LoGU: Long-form Generation with Uncertainty Expressions (2025.acl-long)
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Ruihan Yang, Caiqi Zhang, Zhisong Zhang, Xinting Huang, Sen Yang, Nigel Collier, Dong Yu, Deqing Yang
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