# GRS-QA -- Graph Reasoning-Structured Question Answering Dataset

Source: [GRS-QA -- Graph Reasoning-Structured Question Answering Dataset](<https://www.sota2.com/research/paper/68fa75a11e209131cea6913c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:38:44.922Z

Authors: Anish Pahilajani, Devasha Trivedi, Jincen Shuai, Khin S. Yone, Samyak Rajesh Jain, Namyong Park, Ryan A. Rossi, Nesreen K. Ahmed, Franck Dernoncourt, Yu Wang

Published: 2024-11-01T05:14:03.000Z

## Abstract

Large Language Models (LLMs) have excelled in multi-hop question-answering (M-QA) due to their advanced reasoning abilities. However, the impact of the inherent reasoning structures on LLM M-QA performance remains unclear, largely due to the absence of QA datasets that provide fine-grained reasoning structures. To address this gap, we introduce the Graph Reasoning-Structured Question Answering Dataset (GRS-QA), which includes both semantic contexts and reasoning structures for QA pairs. Unlike existing M-QA datasets, where different reasoning structures are entangled together, GRS-QA explicitly captures intricate reasoning pathways by constructing reasoning graphs, where nodes represent textual contexts and edges denote logical flows. These reasoning graphs of different structures enable a fine-grained evaluation of LLM reasoning capabilities across various reasoning structures. Our empirical analysis reveals that LLMs perform differently when handling questions with varying reasoning structures. This finding facilitates the exploration of textual structures as compared with semantics.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2411.00369>)

[PDF](<https://arxiv.org/pdf/2411.00369.pdf>)