Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries
<|reference_start|>Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries: Few-shot dialogue state tracking (DST) with Large Language Models (LLM) relies on an effective and efficient conversation retriever to find similar in-context examples for prompt learning. Previous works use raw dialogue context as search keys and queries, and a retriever is fine-tuned with annotated dialogues to achieve superior performance. However, the approach is less suited for scaling to new domains or new annotation languages, where fine-tuning data is unavailable. To address this problem, we handle the task of conversation retrieval based on text summaries of the conversations. A LLM-based conversation summarizer is adopted for query and key generation, which enables effective maximum inner product search. To avoid the extra inference cost brought by LLM-based conversation summarization, we further distill a light-weight conversation encoder which produces query embeddings without decoding summaries for test conversations. We validate our retrieval approach on MultiWOZ datasets with GPT-Neo-2.7B and LLaMA-7B/30B. The experimental results show a significant improvement over relevant baselines in real few-shot DST settings.<|reference_end|>
@article{lee2024effective,
    title={Effective and Efficient Conversation Retrieval for Dialogue State
  Tracking with Implicit Text Summaries},
    author={Seanie Lee, Jianpeng Cheng, Joris Driesen, Alexandru Coca, Anders
  Johannsen},
    journal={arXiv preprint arXiv:2402.13043},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13043},
    primaryClass={cs.CL}
}