Time-CoT for Enhancing Time Reasoning Factual Question Answering in Large Language Models

Question-answering tasks are one of the most important tasks in natural language processing (NLP). Recently, large language models (LLMs) have demonstrated outstanding performance, but our experiments reveal that such factual questions involving time reasoning still pose a challenge for existing LLMs. This is primarily attributed to the insensitivity of LLMs to numbers and inability to comprehend the implicit meaning of time, leading to incorrect responses. In order to overcome these limitations, we propose Time Chain-of-Thought (Time-CoT). Specifically, Time-CoT represents the thought process in answering factual questions involving time reasoning and serves as a prompt for LLMs, including explicitation of the implicit "time parameter", the description of the "standard question entity" along the dimension of the timeline and reasoning based on the "time parameter". The aim is to stimulate its potential to answer such questions without external knowledge. We evaluate Time-CoT on three related question-answering datasets and achieve significant improvements, with the most significant improvement of up to 10.87% over strong baselines.