GRS-QA -- Dataset voor Vraag-antwoord met Grafische Redenering
GRS-QA -- Graph Reasoning-Structured Question Answering Dataset
Samenvatting
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.