Masking in Multi-hop QA: An Analysis of How Language Models Perform with
  Context Permutation
Authors: name: Wenyu Huang, name: Pavlos Vougiouklis, name: Mirella Lapata, name: Jeff Z. Pan

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.