GRS-QA -- Dataset voor Vraag-antwoord met Grafische Redenering

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

November 1, 2024
Auteurs: Anish Pahilajani, Devasha Trivedi, Jincen Shuai, Khin S. Yone, Samyak Rajesh Jain, Namyong Park, Ryan A. Rossi, Nesreen K. Ahmed, Franck Dernoncourt, Yu Wang
cs.AI

Samenvatting

Grote Taalmodellen (LLM's) hebben uitgeblonken in meerstapsvraagbeantwoording (M-QA) vanwege hun geavanceerde redeneervermogen. De impact van de inherente redeneerstructuren op de prestaties van LLM M-QA blijft echter onduidelijk, grotendeels door het ontbreken van QA-datasets die gedetailleerde redeneerstructuren bieden. Om deze lacune aan te pakken, introduceren we de Grafische Redeneer-Gestructureerde Vraag-Antwoord Dataset (GRS-QA), die zowel semantische contexten als redeneerstructuren voor QA-paren bevat. In tegenstelling tot bestaande M-QA-datasets, waar verschillende redeneerstructuren door elkaar lopen, legt GRS-QA expliciet complexe redeneerpaden vast door redeneergrafen te construeren, waarbij knooppunten tekstuele contexten vertegenwoordigen en randen logische stromen aangeven. Deze redeneergrafen van verschillende structuren maken een gedetailleerde evaluatie van LLM-redeneervermogens over verschillende redeneerstructuren mogelijk. Onze empirische analyse onthult dat LLM's verschillend presteren bij het behandelen van vragen met verschillende redeneerstructuren. Deze bevinding vergemakkelijkt de verkenning van tekstuele structuren in vergelijking met semantiek.
English
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.

Summary

AI-Generated Summary

Paper Overview

This paper introduces the Graph Reasoning-Structured Question Answering Dataset (GRS-QA) to evaluate Large Language Models (LLMs) in multi-hop question-answering (M-QA) tasks. It explores how reasoning structures impact LLM performance and provides detailed reasoning graphs for analysis, offering new insights into LLM reasoning capabilities.

Core Contribution

  • Introduction of GRS-QA dataset with reasoning graphs for detailed analysis of LLM reasoning.
  • Evaluation of LLM performance based on reasoning structures.
  • Comparison of LLM performance on different reasoning graph types.
  • Exploration of the impact of negative reasoning graphs on LLM performance.
  • Categorization of reasoning graphs into four main types.

Research Context

  • Addresses the lack of QA datasets with fine-grained reasoning structures.
  • Focuses on enhancing LLM performance in M-QA tasks.
  • Compares GRS-QA with existing multi-hop QA datasets.
  • Evaluates LLM performance using retrieval benchmarks.
  • Explores the influence of reasoning structures on LLM reasoning.

Keywords

Large Language Models, Multi-hop Question Answering, Reasoning Structures, Graph Reasoning-Structured Question Answering Dataset, LLM Performance Evaluation

Background

This paper aims to improve LLM performance in M-QA tasks by introducing reasoning graphs through the GRS-QA dataset. The lack of QA datasets with detailed reasoning structures prompted this research to explore how different reasoning structures impact LLM reasoning capabilities.

Research Gap

Absence of QA datasets with fine-grained reasoning structures. Limited understanding of how reasoning structures affect LLM performance.

Technical Challenges

Creating reasoning graphs for each QA pair. Analyzing the impact of reasoning structures on LLM performance.

Prior Approaches

Existing solutions lack detailed reasoning structures for QA pairs. Limited exploration of the influence of reasoning structures on LLM reasoning.

Methodology

The methodology involves constructing reasoning graphs for QA pairs, categorizing them into different types, and evaluating LLM performance based on these structures.

Theoretical Foundation

Utilizes reasoning graphs to represent logical flows in QA pairs. Analyzes LLM performance based on reasoning structures.

Technical Architecture

Reasoning graphs constructed with nodes representing textual contexts and edges denoting logical flows. Different types of reasoning graphs categorized based on their logical structures.

Implementation Details

Utilizes existing multi-hop QA datasets to build reasoning graphs. Includes both positive and negative reasoning graphs for analysis.

Innovation Points

Introduction of GRS-QA dataset with reasoning graphs. Exploration of the impact of reasoning structures on LLM performance.

Experimental Validation

The experimental validation involves evaluating LLM performance using the GRS-QA dataset and comparing it with existing multi-hop QA datasets.

Setup

Utilizes datasets like HotpotQA, MuSiQue, and 2WikiMultiHopQA to construct reasoning graphs. Includes metadata for each QA pair and categorization of reasoning graphs.

Metrics

Evaluation metrics include recall, F1-score, and precision. Comparison of LLM performance on different reasoning graph types.

Results

LLM performance benchmark using models like Llama3(8B Instruct), GPT-3.5, and GPT4o-mini. Structured reasoning graphs improve LLM performance.

Comparative Analysis

Comparison of LLM performance on positive and negative reasoning graphs. Exploration of different retrieval configurations.

Impact and Implications

The GRS-QA dataset offers insights into LLM reasoning capabilities and the impact of reasoning structures on performance, paving the way for future research and practical applications.

Key Findings

Structured reasoning graphs enhance LLM performance. Negative reasoning graphs may impact LLM performance negatively.

Limitations

Imbalanced distribution of graph types in the dataset. Need for future work on generating synthetic data and domain segmentation.

Future Directions

Exploration of diverse negative reasoning graph structures. Further benchmarking with different model architectures.

Practical Significance

Enhanced LLM performance in complex reasoning tasks. Potential applications in domain-specific question-answering tasks.

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