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    <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.</abstract>
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%0 Conference Proceedings
%T TRAM: Benchmarking Temporal Reasoning for Large Language Models
%A Wang, Yuqing
%A Zhao, Yun
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F wang-zhao-2024-tram
%X 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.
%U https://aclanthology.org/2024.findings-acl.382
%P 6389-6415

Markdown (Informal)

[TRAM: Benchmarking Temporal Reasoning for Large Language Models](https://aclanthology.org/2024.findings-acl.382) (Wang & Zhao, Findings 2024)

• TRAM: Benchmarking Temporal Reasoning for Large Language Models (Wang & Zhao, Findings 2024)

ACL

• Yuqing Wang and Yun Zhao. 2024. TRAM: Benchmarking Temporal Reasoning for Large Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 6389–6415, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.