UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorization
- URL: http://arxiv.org/abs/2407.03525v3
- Date: Wed, 18 Dec 2024 20:32:35 GMT
- Title: UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorization
- Authors: Md Nayem Uddin, Amir Saeidi, Divij Handa, Agastya Seth, Tran Cao Son, Eduardo Blanco, Steven R. Corman, Chitta Baral,
- Abstract summary: This paper introduces UnSeenTimeQA, a novel data contamination-free time-sensitive question-answering benchmark.
It differs from existing TSQA benchmarks by avoiding web-searchable queries grounded in the real-world.
We present a series of time-sensitive event scenarios based on synthetically generated facts.
- Score: 34.257914212541394
- License:
- Abstract: This paper introduces UnSeenTimeQA, a novel data contamination-free time-sensitive question-answering (TSQA) benchmark. It differs from existing TSQA benchmarks by avoiding web-searchable queries grounded in the real-world. We present a series of time-sensitive event scenarios based on synthetically generated facts. It requires large language models (LLMs) to engage in genuine temporal reasoning without depending on the factual knowledge acquired during the pre-training phase. We designed three types of time-sensitive questions to test LLMs' temporal reasoning abilities over sequential and parallel event occurrences. Our evaluation of five LLMs on synthetic fact-based TSQA reveals mixed results: while they perform well on simpler subsets, their overall performance remains inferior as compared to real-world fact-based TSQA. Error analysis of LLM-generated reasoning chains indicates that LLMs face difficulties in reasoning over long-range event dependencies and parallel event timelines that unfold concurrently.
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