Semantic Conflicts Benchmark: A Benchmark Dataset for Evaluating Large Language Models' Ability to Detect Semantic Conflicts

This open-source benchmark dataset is specifically designed to evaluate Large Language Models (LLMs) ability to identify semantic conflicts across domains, documents, and evolving knowledge bases. It provides a standardized evaluation tool for research on model factual consistency, and helps optimize scenarios such as RAG and knowledge graph construction.

In today's era of widespread LLM applications, semantic conflict is an often-overlooked yet crucial issue. When models encounter information from different sources, times, or contexts, logical contradictions may arise. Failure to effectively identify and handle these conflicts can lead to factual errors, logical confusion, or even harmful outputs. Its manifestations are diverse: contradictory attributes of the same entity, conflicts in knowledge base updates, semantic differences of terms across domains, etc., which are particularly prominent in scenarios like RAG, multi-document summarization, and knowledge graph construction.

This benchmark is developed and maintained by vivekkrishna, and is an open-source evaluation tool for LLM semantic conflict detection. Project URL: https://github.com/vivekkrishna/semantic-conflicts-benchmark. It covers practical scenarios such as cross-domain conflicts, intra-document conflicts, and temporal conflicts in evolving knowledge bases. Through systematic case design, it helps analyze the strengths and weaknesses of models in handling complex semantic relationships.

1. Cross-domain Conflict: The same concept has different definitions in different domains (e.g., the financial vs. geographical meanings of "bank"), and models need to distinguish based on context; 2. Inter-document Conflict: Multiple documents describe the same fact differently, and models need to identify inconsistencies instead of blindly merging; 3. Knowledge Evolution Conflict: Knowledge updates over time (e.g., scientific discoveries, policy changes), and models need to understand timeliness and identify conflicts between outdated information and current facts.

A rigorous methodology is adopted to ensure credibility: 1. Structured Test Cases: Each case includes clear inputs, expected results, and evaluation criteria, covering explicit to implicit conflicts; 2. Multi-dimensional Metrics: Evaluate conflict location accuracy, explanation quality, uncertainty calibration, and appropriateness of handling strategies; 3. Extensible Framework: Modular design supports adding new cases or custom metrics as research evolves.

1. RAG System Optimization: Evaluate RAG's performance in handling conflicts in retrieval results and optimize conflict detection and resolution modules; 2. Knowledge Graph Quality Assurance: Evaluate the conflict identification ability of automated extraction and fusion algorithms to improve data quality; 3. Model Selection Reference: Provide an objective basis for model comparison in complex information scenarios, helping to select appropriate base models.

It uses a clear data format and concise API design to lower the usage threshold. Researchers can obtain detailed evaluation reports by preparing model outputs in the specified format. The project also provides rich sample code and documentation to help quickly build evaluation processes, suitable for academic research and engineering applications.

Semantic conflict detection is an important dimension to measure LLM reliability. As models are deployed to critical scenarios, the evaluation of their ability to handle conflicting information becomes increasingly important. This benchmark provides infrastructure for the research field, and we look forward to more researchers conducting work based on it to jointly promote progress in LLM factual consistency and logical reliability.