Context Matters: An Empirical Study of the Impact of Contextual Information in Temporal Question Answering Systems

• URL: http://arxiv.org/abs/2406.19538v1
• Date: Thu, 27 Jun 2024 21:31:30 GMT
• Title: Context Matters: An Empirical Study of the Impact of Contextual Information in Temporal Question Answering Systems
• Authors: Dan Schumacher, Fatemeh Haji, Tara Grey, Niharika Bandlamudi, Nupoor Karnik, Gagana Uday Kumar, Jason Cho-Yu Chiang, Paul Rad, Nishant Vishwamitra, Anthony Rios,
• Abstract summary: This paper empirically examines the robustness of temporal question-answering systems trained on various context types. We show that training with a mix of these contexts enhances model robustness and accuracy. We introduce two new context-rich TQA datasets, ContextAQA and ContextTQE, and provide comprehensive evaluations and guidelines for training robust TQA models.
• Score: 7.393290178125003
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) often struggle with temporal reasoning, crucial for tasks like historical event analysis and time-sensitive information retrieval. Despite advancements, state-of-the-art models falter in handling temporal information, especially when faced with irrelevant or noisy contexts. This paper addresses this gap by empirically examining the robustness of temporal question-answering (TQA) systems trained on various context types, including relevant, irrelevant, slightly altered, and no context. Our findings indicate that training with a mix of these contexts enhances model robustness and accuracy. Additionally, we show that the position of context relative to the question significantly impacts performance, with question-first positioning yielding better results. We introduce two new context-rich TQA datasets, ContextAQA and ContextTQE, and provide comprehensive evaluations and guidelines for training robust TQA models. Our work lays the foundation for developing reliable and context-aware temporal QA systems, with broader implications for enhancing LLM robustness against diverse and potentially adversarial information.

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