Enhancing Temporal Understanding in LLMs for Semi-structured Tables

Abstract

Temporal reasoning over tabular data presents substantial challenges for large language models (LLMs), as evidenced by recent research. In this study, we conduct a comprehensive analysis of temporal datasets to pinpoint the specific limitations of LLMs. Our investigation leads to enhancements in TempTabQA, a benchmark specifically designed for tabular temporal question answering. We provide critical insights for enhancing LLM performance in temporal reasoning tasks with tabular data. Furthermore, we introduce a novel approach, C.L.E.A.R to strengthen LLM capabilities in this domain. Our findings demonstrate that our method improves evidence-based reasoning across various models. Additionally, our experimental results reveal that indirect supervision with auxiliary unstructured data (TRAM) substantially boosts model performance in these tasks. This work contributes to a deeper understanding of LLMs' temporal reasoning abilities over tabular data and promotes advancements in their application across diverse fields.

C.L.E.A.R Prompting: A Structured Approach to Complex Question Answering

To enhance the accuracy of large language models (LLMs) in handling temporal reasoning over semi-structured data, we introduce the C.L.E.A.R prompting framework. This method systematically reduces errors such as hallucinations, incomplete evidence extraction, and misinterpretations by breaking down the reasoning process into five structured steps:

Comprehend : Understand the question by applying domain knowledge, correctly interpreting its temporal components, and identifying key details.
Locate : Extract only the relevant rows from the data table, ensuring that all necessary evidence is collected to answer the question accurately.
Examine : Decompose complex queries into manageable sub-questions, making temporal calculations clearer and reducing reasoning errors.
Analyze : Answer each sub-question using extracted evidence, applying logical reasoning and ensuring consistency with the available data.
Resolve : Synthesize the answers from the sub-questions into a final response, ensuring clarity, correctness, and logical coherence.

The C.L.E.A.R approach provides a structured, step-by-step method that improves LLMs’ reasoning capabilities, minimizing common errors and enhancing reliability in complex question answering.

How Does C.L.E.A.R Work?

Here is an example where a given table and structured reasoning are used to answer a question through C.L.E.A.R Prompting. This step-by-step approach helps guide the model in the right direction by ensuring proper comprehension, relevant evidence extraction, logical decomposition, and accurate reasoning. The breakdown below demonstrates how each stage of C.L.E.A.R contributes to forming a well-supported final answer.

Auxiliary Data Fine-Tuning for Enhanced Temporal Reasoning

Fine-tuning with auxiliary datasets significantly enhances a model’s ability to process temporal information, improving its performance in ordering, frequency, duration, and logical deduction over time-based data. While C.L.E.A.R Prompting provides a structured reasoning approach, intrinsic model improvements require fine-tuning to refine context understanding and evidence-based question answering.

Our evaluation demonstrates that fine-tuning with diverse datasets like TRAM and TempTabQA leads to better handling of complex temporal reasoning tasks. Among the datasets tested, TRAM proves especially effective due to its wide range of temporal challenges, helping models generalize across different formats. The results highlight that fine-tuning not only boosts accuracy but also strengthens a model’s ability to process nuanced temporal relationships, making it more reliable across various tasks.

By leveraging rich and diverse auxiliary data, fine-tuning provides an adaptable approach that improves reasoning beyond task-specific training, reinforcing the model’s ability to handle complex queries with higher precision and consistency.