MDBench: A Synthetic Multi-Document Reasoning Benchmark Generated with Knowledge Guidance

TL;DR

MDBench is a synthetic multi-document reasoning benchmark created using knowledge-guided generation, providing a controllable and challenging evaluation for large language models in multi-document reasoning tasks.

Contribution

The paper introduces MDBench, a novel synthetic dataset for multi-document reasoning evaluation, generated through a knowledge-guided process that enables targeted and efficient benchmark creation.

Findings

MDBench challenges current LLMs even with short document sets.

Knowledge-guided generation allows targeted analysis of reasoning capabilities.

The benchmark can be quickly adapted for new challenges.

Abstract

Natural language processing evaluation has made significant progress, largely driven by the proliferation of powerful large language mod-els (LLMs). New evaluation benchmarks are of increasing priority as the reasoning capabilities of LLMs are expanding at a rapid pace. In particular, while multi-document (MD) reasoning is an area of extreme relevance given LLM capabilities in handling longer-context inputs, few benchmarks exist to rigorously examine model behavior in this setting. Moreover, the multi-document setting is historically challenging for benchmark creation due to the expensive cost of annotating long inputs. In this work, we introduce MDBench, a new dataset for evaluating LLMs on the task of multi-document reasoning. Notably, MDBench is created through a novel synthetic generation process, allowing us to controllably and efficiently generate challenging document sets and the corresponding question-answer (QA) examples. Our novel technique operates on condensed structured seed knowledge, modifying it through LLM-assisted edits to induce MD-specific reasoning challenges. We then convert this structured knowledge into a natural text surface form, generating a document set and corresponding QA example. We analyze the behavior of popular LLMs and prompting techniques, finding that MDBENCH poses significant challenges for all methods, even with relatively short document sets. We also see our knowledge-guided generation technique (1) allows us to readily perform targeted analysis of MD-specific reasoning capabilities and (2) can be adapted quickly to account for new challenges and future modeling improvements.