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.

Related papers

• Mitigating Knowledge Conflicts in Language Model-Driven Question Answering [15.29366851382021]
  Two fundamental knowledge sources play crucial roles in document-based question answering and document summarization systems. Recent studies revealed a significant challenge: when there exists a misalignment between the model's inherent knowledge and the ground truth answers in training data, the system may exhibit problematic behaviors during inference. Our investigation proposes a strategy to minimize hallucination by building explicit connection between source inputs and generated outputs.
  arXiv  Detail & Related papers  (2024-11-18T07:33:10Z)
• On the Loss of Context-awareness in General Instruction Fine-tuning [101.03941308894191]
  We investigate the loss of context awareness after supervised fine-tuning. We find that the performance decline is associated with a bias toward different roles learned during conversational instruction fine-tuning. We propose a metric to identify context-dependent examples from general instruction fine-tuning datasets.
  arXiv  Detail & Related papers  (2024-11-05T00:16:01Z)
• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context. We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters. We propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering [23.98067169669452]
  Time-Sensitive Question Answering (TSQA) demands the effective utilization of specific temporal contexts. We propose a novel framework that enhances temporal awareness and reasoning through Temporal Information-Aware Embedding and Granular Contrastive Reinforcement Learning.
  arXiv  Detail & Related papers  (2024-09-25T13:13:21Z)
• QTG-VQA: Question-Type-Guided Architectural for VideoQA Systems [3.486120902611884]
  This paper explores the significance of different question types for VQA systems and their impact on performance. We propose QTG-VQA, a novel architecture that incorporates question-type-guided attention and adaptive learning mechanism.
  arXiv  Detail & Related papers  (2024-09-14T07:42:41Z)
• QUITO-X: A New Perspective on Context Compression from the Information Bottleneck Theory [66.01597794579568]
  We introduce information bottleneck theory (IB) to model the problem. We propose a cross-attention-based approach to approximate mutual information in IB. Our method achieves a 25% increase in compression rate compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2024-08-20T02:44:45Z)
• Synthetic Context Generation for Question Generation [6.226609932118123]
  This paper investigates training QG models using synthetic contexts generated by large language models. We find that contexts are essential for QG tasks, even if they are synthetic.
  arXiv  Detail & Related papers  (2024-06-19T03:37:52Z)
• Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop QA Dataset and Pseudo-Instruction Tuning [73.51314109184197]
  It is crucial for large language models (LLMs) to understand the concept of temporal knowledge. We propose a complex temporal question-answering dataset Complex-TR that focuses on multi-answer and multi-hop temporal reasoning.
  arXiv  Detail & Related papers  (2023-11-16T11:49:29Z)
• Lost in the Middle: How Language Models Use Long Contexts [88.78803442320246]
  We analyze the performance of language models on two tasks that require identifying relevant information in their input contexts. We find that performance can degrade significantly when changing the position of relevant information. Our analysis provides a better understanding of how language models use their input context and provides new evaluation protocols for future long-context language models.
  arXiv  Detail & Related papers  (2023-07-06T17:54:11Z)
• Stateful Offline Contextual Policy Evaluation and Learning [88.9134799076718]
  We study off-policy evaluation and learning from sequential data. We formalize the relevant causal structure of problems such as dynamic personalized pricing. We show improved out-of-sample policy performance in this class of relevant problems.
  arXiv  Detail & Related papers  (2021-10-19T16:15:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.