Papers with question reformulation
Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension (D19-58)
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| Challenge: | Existing question reformulation models are based on supervised question labels without considering feedback information from answers. |
| Approach: | They propose a question reformulation model that integrates conversational history information with reinforcement learning. |
| Outcome: | The proposed model is more effective in conversational machine comprehension with reinforcement learning. |
Multi-Task Learning of Query Generation and Classification for Generative Conversational Question Rewriting (2023.findings-emnlp)
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| Challenge: | Existing approaches to identifying ambiguous questions as part of a conversation have not addressed this challenge. |
| Approach: | They propose a multi-task learning approach that uses a text generation model for question rewriting and classification. |
| Outcome: | The proposed approach outperforms single-task learning baselines on three LIF test sets. |
Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception (2025.acl-long)
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| Challenge: | Large language models (LLMs) exhibit impressive performance across diverse tasks but struggle to accurately gauge their knowledge boundaries. |
| Approach: | They propose Consistency-based Confidence Calibration (C3) which assesses confidence consistency through question reformulation to improve LLMs’ ability to recognize their knowledge gaps. |
| Outcome: | The proposed method improves the unknown perception rate by 5.6% on NQ and 4.9% on HotpotQA. |