DialogBench: Evaluating LLMs as Human-like Dialogue Systems

arXiv:2311.01677v2 Announce Type: replace
Abstract: Large language models (LLMs) have achieved remarkable breakthroughs in new dialogue capabilities by leveraging instruction tuning, which refreshes human impressions of dialogue systems. The long-standing goal of dialogue systems is to be human-like enough to establish long-term connections with users. Therefore, there has been an urgent need to evaluate LLMs as human-like dialogue systems. In this paper, we propose DialogBench, a dialogue evaluation benchmark that contains 12 dialogue tasks to probe the capabilities of LLMs …

abstract arxiv capabilities cs.ai cs.cl dialogue human human-like impressions language language models large language large language models llms long-term systems type