New Benchmark Framework for Evaluating Graph Reasoning in Large Language Models
A new framework has been developed by researchers for creating intricate graph reasoning benchmarks to assess large language models (LLMs). This framework, detailed in an arXiv paper (2608.12391), aims to overcome the shortcomings of current benchmarks by enhancing coverage across five dimensions: task complexity, graph size, task description, task source, and graph loading. Utilizing a semi-automatic, five-step process, it leverages an LLM-driven data generator to create task descriptions, graph data, reference solutions, evaluation standards, and more. This method seeks to unify evaluations of text-based and code-based reasoning, addressing the difficulties associated with manual construction and limited data complexity. This advancement is crucial for AI research, as graph reasoning is vital for evaluating LLM performance in structured scenarios and long-input contexts.
Key facts
- Framework is semi-automatic and five-stage.
- Expands coverage along five dimensions: Graph Size, Task Complexity, Task Description, Graph Loading, and Task Source.
- Uses an LLM-based data generator.
- Generates task descriptions, graph data, reference solutions, graph-loading scripts, question forms, and evaluation standards.
- Provides unified evaluation across text-based and code-based reasoning modes.
- Addresses limitations of existing benchmarks: limited data complexity, heavy manual construction, lack of unified evaluation.
- Paper available on arXiv with ID 2608.12391.
- Graph reasoning is used to evaluate reasoning ability of LLMs.
Entities
Institutions
- arXiv