Papers with Conversational dense retrieval
Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation (2024.acl-long)
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| Challenge: | Existing conversational dense retrieval models view a conversation as a fixed sequence of questions and responses, and these alternate conversations are unrecorded. |
| Approach: | They propose a framework for generalizing Conversational dense retrieval via LLM-cognition data Augmentation (ConvAug) they first generate multi-level augmented conversations to capture the diverse nature of conversational contexts. |
| Outcome: | The proposed framework generalizes Conversational dense retrieval via LLM-cognition data Augmentation on four public datasets. |
Interpreting Conversational Dense Retrieval by Rewriting-Enhanced Inversion of Session Embedding (2024.acl-long)
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| Challenge: | Conversational dense retrieval models lack interpretability, hindering intuitive understanding of model behaviors . a major limitation of conversational dense search is their lack of interpretability . |
| Approach: | They propose to transform opaque session embeddings into explicit interpretable text . they propose to incorporate external interpretable query rewrites into the transformation process . |
| Outcome: | The proposed approach yields more interpretable text and preserves original retrieval performance over baselines. |