Modeling Multi-hop Question Answering as Single Sequence Prediction

TL;DR

This paper introduces PathFid, a generative model that linearizes multi-hop reasoning paths into a single sequence, improving performance and interpretability in multi-hop question answering tasks.

Contribution

PathFid extends generative QA to multi-hop reasoning by explicitly modeling reasoning paths and encoding cross-passage interactions, achieving better accuracy and interpretability.

Findings

Significant performance improvements on HotpotQA and IIRC datasets.

Enhanced interpretability and factual grounding of answers.

Effective modeling of complex multi-hop reasoning processes.

Abstract

Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state of the art on single-hop QA. However, the complexity of multi-hop QA hinders the effectiveness of the generative QA approach. In this work, we propose a simple generative approach (PathFid) that extends the task beyond just answer generation by explicitly modeling the reasoning process to resolve the answer for multi-hop questions. By linearizing the hierarchical reasoning path of supporting passages, their key sentences, and finally the factoid answer, we cast the problem as a single sequence prediction task. To facilitate complex reasoning with multiple clues, we further extend the unified flat representation of multiple input documents by encoding cross-passage interactions. Our extensive experiments demonstrate that PathFid leads to strong performance gains on two multi-hop QA datasets: HotpotQA and IIRC. Besides the performance gains, PathFid is more interpretable, which in turn yields answers that are more faithfully grounded to the supporting passages and facts compared to the baseline Fid model.