Reasoning in Conversation: Solving Subjective Tasks through Dialogue Simulation for Large Language Models

arXiv:2402.17226v1 Announce Type: new
Abstract: Large Language Models (LLMs) have achieved remarkable performance in objective tasks such as open-domain question answering and mathematical reasoning, which can often be solved through recalling learned factual knowledge or chain-of-thought style reasoning. However, we find that the performance of LLMs in subjective tasks is still unsatisfactory, such as metaphor recognition, dark humor detection, etc. Compared to objective tasks, subjective tasks focus more on interpretation or emotional response rather than a universally accepted reasoning pathway. …

abstract arxiv conversation cs.cl dialogue domain knowledge language language models large language large language models llms mathematical reasoning performance question question answering reasoning simulation style tasks thought through type