Towards Explainable Temporal Reasoning in Large Language Models: A Structure-Aware Generative Framework (2025)

Abstract: 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 robust generalization capabilities. Our dataset and code are available at https://github.com/carryTatum/GETER.

## introduction temporal reasoning (tr) is a fundamental cognitive skill essential for understanding complex tasks like planning and causal relation discovery @xcite . in natural language processing (nlp), temporal reasoning refers to a model's capability to effectively comprehend, represent, and predict time-sensitive contexts @xcite . this capability is critical for real-world applications that depend on temporal data, including search engine recommendations @xcite and news article aggregation @xcite . recently, large language models (llms) have demonstrated remarkable performance in tackling complex tasks @xcite @xcite @xcite . building on this success, recent studies have increasingly focused on exploring the tr capabilities of llms. these works primarily adopt general approaches to evaluate and enhance the tr capabilities of llms. for instance, @xcite and @xcite design time-sensitive queries to benchmark llms, while @xcite and chu et al. (2024) extend these efforts by using prompting strategies like in-context learning (icl) and chain-of-thought (cot) reasoning for comprehensive evaluation. furthermore, @xcite and @xcite employ icl with prompts containing intermediate reasoning steps to guide models, while @xcite and @xcite adopt fine-tuning methods, training llms on reasoning process texts to enable them to produce accurate answers. although existing methods have explored llms' potential in temporal reasoning, they exceedingly focus on improving performance, often overlooking the explainable reasoning processes behind the results, as illustrated in figure 1(a ). the study of explainable temporal reasoning is crucial, as it promotes transparency, enhances effectiveness, and fosters trust in understanding temporal dynamics. moreover, with their impressive semantic understanding and generation capabilities, llms are uniquely positioned to address the challenges of explainable reasoning @xcite , as they can generate flexible, humanreadable reasoning processes. therefore, we posit the following research question to guide our study: can llms effectively make accurate predictions and clearly explaining their reasoning processes in complex temporal reasoning scenarios? to address this challenge, we propose the etr benchmark, a comprehensive benchmark for explainable temporal reasoning. specifically, etr consists of five datasets covering a wide range of temporal granularities (minutes, days, and years). each instance is represented as a triple @xmath0, reasoning chains text, explanation @xmath1. the explanation text is synthesized using @xcite with constrained generation prompt protocols, taking the query and reasoning chains as input. the resulting explanation text effectively integrates both the original gold prediction and the underlying reasoning processes. etr aims to challenge llms not only to predict future events from the given reasoning chains text but also to generate explanations of their reasoning processes. building on this benchmark, we identify that the key to achieving explainable temporal reasoning lies in enabling llms to capture structured patterns that reflect the relationships and dynamics between events over time. as shown in figure 1(b) , our findings reveal that llms struggle to deliver convincing explanations when relying solely on textual information, a challenge (e.g. hallucinations) also highlighted in previous analyses @xcite . to address this challenge, we propose a novel structure-aware generative framework geter, which advances explainable temporal reasoning by effectively bridging the gap between graph structures and text. specifically, we leverage tkgs to develop a temporal encoder that captures structural information. subsequently, the encoder converts the query and reasoning chains into a soft graph token, which is then mapped into the llm's text space via a lightweight adapter. finally, llm can generate explanation text by integrating the soft graph token with instruction-tuning prompt tokens, seamlessly combining structural and contextual semantic information. experimental results show that our proposed geter achieves state-of-the-art performance. in summary, the contributions of this paper are as follows: ## methodology in this section, we present geter, a novel structure-aware generative framework that integrates graph structures with text for explainable temporal reasoning. the overall architecture of our proposed model is illustrated in figure 3 . specifically, we first leverage a temporal encoder (sec. 4.1) to obtain structural embeddings for both entities and relations. subsequently, we introduce a structure-text prefix adapter as described in sec. 4.2 to map graph structure features into the text embedding space. finally, we apply an instruction-tuning strategy (sec. 4.3) to effectively adapt the model to the explainable temporal reasoning task. ## conclusion we introduce a comprehensive benchmark covering a wide range of temporal granularities for systematically evaluating llms' explainable temporal reasoning. to address the challenge of llms struggling to deliver convincing explanations, we propose a novel structure-aware generative framework geter, which effectively bridges the gap between graph structures and text by through a lightweight structure-text adapter. extensive experiments validate the effectiveness and robustness of our proposed geter.