AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models
This work addresses a data bottleneck for researchers in conversational AI, though it is incremental as it builds on existing LLM capabilities.
The paper tackles the scarcity of training data for information-seeking conversations by proposing AutoConv, a method that uses large language models to generate synthetic conversations, resulting in substantial improvements over baselines on two datasets and reducing reliance on human annotation.
Information-seeking conversation, which aims to help users gather information through conversation, has achieved great progress in recent years. However, the research is still stymied by the scarcity of training data. To alleviate this problem, we propose AutoConv for synthetic conversation generation, which takes advantage of the few-shot learning ability and generation capacity of large language models (LLM). Specifically, we formulate the conversation generation problem as a language modeling task, then finetune an LLM with a few human conversations to capture the characteristics of the information-seeking process and use it for generating synthetic conversations with high quality. Experimental results on two frequently-used datasets verify that AutoConv has substantial improvements over strong baselines and alleviates the dependence on human annotation. In addition, we also provide several analysis studies to promote future research.