Exploring the Dialogue Comprehension Ability of Large Language Models

The recent emergence of large language models (LLMs) have attracted considerable attention. LLMs may interact with users in the form of dialogue and generate responses following their instructions, which naturally require dialogue comprehension abilities. Without correct comprehension of the dialogue, the model may inevitably generate incorrect responses. However, dialogue comprehension is a general language ability which is hard to be evaluated directly. In this work, we propose to perform the evaluation with the help of the dialogue summarization task. Beside evaluating and analyzing the dialogue summarization performance (DIAC-Sum), we also derive factual questions from the generated summaries and use them as a more flexible measurement of dialogue comprehension (DIAC-FactQA). Our evaluation shows that, on average, 27% of the summaries generated by LLMs contain factual inconsistency. Even ChatGPT, the strongest evaluated model, has such errors in 16% of its summaries. For answering the factual questions, which is more challenging, the average accuracy of all evaluated LLMs is only 62.8%. Both results indicate serious deficiencies. Detailed analysis shows that the understanding of subject/object of the conversation is still the most challenging problem for LLMs. Furthermore, to stimulate and enhance the dialogue comprehension ability of LLMs, we propose a fine-tuning paradigm with auto-constructed multi-task data. The experimental results demonstrate that our method achieved an accuracy improvement of 8.9% on DIAC-FactQA.