Masking in Multi-hop QA: An Analysis of How Language Models Perform with Context Permutation

• URL: http://arxiv.org/abs/2505.11754v1
• Date: Fri, 16 May 2025 23:29:47 GMT
• Title: Masking in Multi-hop QA: An Analysis of How Language Models Perform with Context Permutation
• Authors: Wenyu Huang, Pavlos Vougiouklis, Mirella Lapata, Jeff Z. Pan,
• Abstract summary: Multi-hop Question Answering (MHQA) adds layers of complexity to question answering, making it more challenging.<n>This paper explores how Language Models respond to multi-hop questions by permuting search results (retrieved documents) under various configurations.
• Score: 56.69064935192318
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-hop Question Answering (MHQA) adds layers of complexity to question answering, making it more challenging. When Language Models (LMs) are prompted with multiple search results, they are tasked not only with retrieving relevant information but also employing multi-hop reasoning across the information sources. Although LMs perform well on traditional question-answering tasks, the causal mask can hinder their capacity to reason across complex contexts. In this paper, we explore how LMs respond to multi-hop questions by permuting search results (retrieved documents) under various configurations. Our study reveals interesting findings as follows: 1) Encoder-decoder models, such as the ones in the Flan-T5 family, generally outperform causal decoder-only LMs in MHQA tasks, despite being significantly smaller in size; 2) altering the order of gold documents reveals distinct trends in both Flan T5 models and fine-tuned decoder-only models, with optimal performance observed when the document order aligns with the reasoning chain order; 3) enhancing causal decoder-only models with bi-directional attention by modifying the causal mask can effectively boost their end performance. In addition to the above, we conduct a thorough investigation of the distribution of LM attention weights in the context of MHQA. Our experiments reveal that attention weights tend to peak at higher values when the resulting answer is correct. We leverage this finding to heuristically improve LMs' performance on this task. Our code is publicly available at https://github.com/hwy9855/MultiHopQA-Reasoning.

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