TComQA: Extracting Temporal Commonsense from Text
This work addresses the need for robust language models by enabling better temporal reasoning, though it is incremental as it builds on existing LLM capabilities and datasets.
The paper tackled the problem of extracting temporal commonsense from text, which is challenging for machines due to its implicit nature, by proposing a pipeline using LLMs to automatically mine such knowledge and construct the TComQA dataset, achieving over 80% precision in extraction and outperforming an LLM fine-tuned on existing datasets in temporal question answering.
Understanding events necessitates grasping their temporal context, which is often not explicitly stated in natural language. For example, it is not a trivial task for a machine to infer that a museum tour may last for a few hours, but can not take months. Recent studies indicate that even advanced large language models (LLMs) struggle in generating text that require reasoning with temporal commonsense due to its infrequent explicit mention in text. Therefore, automatically mining temporal commonsense for events enables the creation of robust language models. In this work, we investigate the capacity of LLMs to extract temporal commonsense from text and evaluate multiple experimental setups to assess their effectiveness. Here, we propose a temporal commonsense extraction pipeline that leverages LLMs to automatically mine temporal commonsense and use it to construct TComQA, a dataset derived from SAMSum and RealNews corpora. TComQA has been validated through crowdsourcing and achieves over 80\% precision in extracting temporal commonsense. The model trained with TComQA also outperforms an LLM fine-tuned on existing dataset of temporal question answering task.