Papers with Long-form question answering
Read before Generate! Faithful Long Form Question Answering with Machine Reading (2022.findings-acl)
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| Challenge: | Long-form question answering (LFQA) generates a paragraph-length answer for a given question. |
| Approach: | They propose a framework that jointly models answer generation and machine reading. |
| Outcome: | The proposed model generates a more factually accurate answer from millions of documents retrieved from a large dataset. |
A Critical Evaluation of Evaluations for Long-form Question Answering (2023.acl-long)
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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 . |
| Approach: | They conduct the first targeted study of the evaluation of long-form answers, covering both human and automatic evaluation practices. |
| Outcome: | The proposed evaluations cover human and automatic evaluations. |
WebCPM: Interactive Web Search for Chinese Long-form Question Answering (2023.acl-long)
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Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie Zhou
| Challenge: | Long-form question answering requires two procedures: information retrieval and information synthesis. |
| Approach: | They propose a Chinese long-form question answering dataset called WebCPM . the dataset is based on a web search interface that engages with a search engine in real time . |
| Outcome: | The proposed dataset generates answers that are no worse than human-written ones . the dataset is the first Chinese LFQA dataset . |
Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing reinforcement learning systems lack verifiable reward mechanisms for long-form question answering . current systems lack reliable long-term answers due to lack of factual content . |
| Approach: | They propose a framework for reinforced verifiable informativeness optimization . it defines informativeness as measurable and externally verifier objective for RL . |
| Outcome: | Experiments show that RioRAG achieves higher factual recall and faithfulness . the proposed framework is based on a framework that uses nugget-centric verification with cross-source checks . |
Retrieving Contextual Information for Long-Form Question Answering using Weak Supervision (2024.findings-emnlp)
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| Challenge: | Existing retrievers for long-form question answering are optimized for information that directly targets the question, missing out on contextual information. |
| Approach: | They propose to use weak supervision techniques to optimize retrieval for contextual information. |
| Outcome: | The proposed techniques improve the end-to-end QA performance on a conversational QA dataset. |
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. |