TRAM: Benchmarking Temporal Reasoning for Large Language Models

• URL: http://arxiv.org/abs/2310.00835v3
• Date: Fri, 31 May 2024 15:36:09 GMT
• Title: TRAM: Benchmarking Temporal Reasoning for Large Language Models
• Authors: Yuqing Wang, Yun Zhao,
• Abstract summary: We introduce TRAM, a temporal reasoning benchmark composed of ten datasets. We evaluate popular language models like GPT-4 and Llama2 in zero-shot and few-shot scenarios. Our findings indicate that the best-performing model lags significantly behind human performance.
• Score: 12.112914393948415
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Reasoning about time is essential for understanding the nuances of events described in natural language. Previous research on this topic has been limited in scope, characterized by a lack of standardized benchmarks that would allow for consistent evaluations across different studies. In this paper, we introduce TRAM, a temporal reasoning benchmark composed of ten datasets, encompassing various temporal aspects of events such as order, arithmetic, frequency, and duration, designed to facilitate a comprehensive evaluation of the TeR capabilities of large language models (LLMs). We evaluate popular LLMs like GPT-4 and Llama2 in zero-shot and few-shot scenarios, and establish baselines with BERT-based and domain-specific models. Our findings indicate that the best-performing model lags significantly behind human performance. It is our aspiration that TRAM will spur further progress in enhancing the TeR capabilities of LLMs.

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