Papers with generating fluent responses
BP4ER: Bootstrap Prompting for Explicit Reasoning in Medical Dialogue Generation (2024.lrec-main)
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| Challenge: | Medical dialogue generation (MDG) has gained increasing attention due to its substantial practical value. |
| Approach: | They propose a method which explicitly models MDG’s multi-step reasoning process and iteratively enhances this reasoning process. |
| Outcome: | The proposed method outperforms state-of-the-art methods across objective and subjective evaluations on two publicly available datasets. |
Model Analysis & Evaluation for Ambiguous Question Answering (2023.findings-acl)
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| Challenge: | Ambiguous questions are a challenge for Question Answering models as they require answers that cover multiple interpretations of the original query. |
| Approach: | They aim to investigate whether model/data scaling improves the answers’ quality and whether automated metrics align with human judgment. |
| Outcome: | The proposed models can generate long-form answers that combine conflicting information and provide valuable insights into the limitations of the current approaches. |
Multi-Source Probing for Open-Domain Conversational Understanding (2023.emnlp-main)
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| Challenge: | Existing models of open-domain dialogue comprehension have limited conversational understanding and response generation. |
| Approach: | They propose a multi-source probing method to probe dialogue comprehension abilities of open-domain dialogue models. |
| Outcome: | The proposed method aggregates features from multiple sources to accomplish diverse task goals and conducts downstream tasks in a generative manner consistent with dialogue model pre-training to leverage model capabilities. |