Papers with multi-turn dialogue generation
Format Inertia: A Failure Mechanism of LLMs in Medical Pre-Consultation (2025.emnlp-industry)
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| Challenge: | Recent advances in Large Language Models have brought significant improvements to various service domains, including chatbots and medical pre-consultation applications. |
| Approach: | They propose a method that rebalances the turn-count distribution of training data to mitigate Format Inertia in medical pre-consultation tasks. |
| Outcome: | The proposed method significantly alleviates Format Inertia in medical pre-consultation tasks. |
ReCoSa: Detecting the Relevant Contexts with Self-Attention for Multi-turn Dialogue Generation (P19-1)
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| Challenge: | Existing hierarchical recurrent encoder-decoder models treat all contexts indiscriminately, which may hurt the following response generation process. |
| Approach: | They propose a hierarchical recurrent encoder-decoder model that treats all contexts indiscriminately and uses a word level LSTM encoder to obtain the initial representation of each context. |
| Outcome: | The proposed model outperforms baseline models on Chinese customer services and English Ubuntu dialogue datasets in terms of both metric-based and human evaluations. |