Towards Explainable Temporal Reasoning in Large Language Models: A Structure-Aware Generative Framework
This work addresses the need for explainable reasoning processes in LLMs for temporal tasks, which is important for users in AI and NLP domains, though it is incremental as it builds on existing methods by adding structural integration.
The paper tackles the problem of explainable temporal reasoning in large language models (LLMs) by introducing a benchmark and a novel structure-aware generative framework called GETER, which integrates graph structures with text to achieve state-of-the-art performance and strong generalization capabilities.
While large language models (LLMs) show great potential in temporal reasoning, most existing work focuses heavily on enhancing performance, often neglecting the explainable reasoning processes underlying the results. To address this gap, we introduce a comprehensive benchmark covering a wide range of temporal granularities, designed to systematically evaluate LLMs' capabilities in explainable temporal reasoning. Furthermore, our findings reveal that LLMs struggle to deliver convincing explanations when relying solely on textual information. To address challenge, we propose GETER, a novel structure-aware generative framework that integrates Graph structures with text for Explainable TEmporal Reasoning. Specifically, we first leverage temporal knowledge graphs to develop a temporal encoder that captures structural information for the query. Subsequently, we introduce a structure-text prefix adapter to map graph structure features into the text embedding space. Finally, LLMs generate explanation text by seamlessly integrating the soft graph token with instruction-tuning prompt tokens. Experimental results indicate that GETER achieves state-of-the-art performance while also demonstrating its effectiveness as well as strong generalization capabilities. Our dataset and code are available at https://github.com/carryTatum/GETER.