Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering

TL;DR

This paper introduces a novel cross-document question answering pre-training objective that enhances multi-document language models' ability to understand and generate across related texts, improving performance on various tasks.

Contribution

The paper proposes a new pre-training method using cross-document QA to improve multi-document modeling, enabling better informational relation recovery and task versatility.

Findings

Up to 7% performance improvement on multi-document tasks

Significantly outperforms zero-shot GPT-3.5 and GPT-4

Enhances both short and long text generation capabilities

Abstract

The integration of multi-document pre-training objectives into language models has resulted in remarkable improvements in multi-document downstream tasks. In this work, we propose extending this idea by pre-training a generic multi-document model from a novel cross-document question answering pre-training objective. To that end, given a set (or cluster) of topically-related documents, we systematically generate semantically-oriented questions from a salient sentence in one document and challenge the model, during pre-training, to answer these questions while "peeking" into other topically-related documents. In a similar manner, the model is also challenged to recover the sentence from which the question was generated, again while leveraging cross-document information. This novel multi-document QA formulation directs the model to better recover cross-text informational relations, and introduces a natural augmentation that artificially increases the pre-training data. Further, unlike prior multi-document models that focus on either classification or summarization tasks, our pre-training objective formulation enables the model to perform tasks that involve both short text generation (e.g., QA) and long text generation (e.g., summarization). Following this scheme, we pre-train our model -- termed QAmden -- and evaluate its performance across several multi-document tasks, including multi-document QA, summarization, and query-focused summarization, yielding improvements of up to 7%, and significantly outperforms zero-shot GPT-3.5 and GPT-4.