MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models
This work addresses a critical gap in evaluating LLMs for temporal reasoning, which is important for NLP researchers and developers, though it is incremental as it focuses on dataset creation and benchmarking.
The authors tackled the insufficiently emphasized temporal comprehension and reasoning abilities of large language models (LLMs) by constructing the MenatQA dataset with 2,853 samples across three temporal factors, and found that most LLMs fall behind smaller temporal reasoning models, showing significant vulnerability to temporal biases.
Large language models (LLMs) have shown nearly saturated performance on many natural language processing (NLP) tasks. As a result, it is natural for people to believe that LLMs have also mastered abilities such as time understanding and reasoning. However, research on the temporal sensitivity of LLMs has been insufficiently emphasized. To fill this gap, this paper constructs Multiple Sensitive Factors Time QA (MenatQA), which encompasses three temporal factors (scope factor, order factor, counterfactual factor) with total 2,853 samples for evaluating the time comprehension and reasoning abilities of LLMs. This paper tests current mainstream LLMs with different parameter sizes, ranging from billions to hundreds of billions. The results show most LLMs fall behind smaller temporal reasoning models with different degree on these factors. In specific, LLMs show a significant vulnerability to temporal biases and depend heavily on the temporal information provided in questions. Furthermore, this paper undertakes a preliminary investigation into potential improvement strategies by devising specific prompts and leveraging external tools. These approaches serve as valuable baselines or references for future research endeavors.