# GLSim: Detecting Object Hallucinations in LVLMs via Global-Local Similarity

## TL;DR

GLSim is a training-free framework that improves detection of object hallucinations in vision-language models by combining global and local similarity signals, leading to more reliable results across scenarios.

## Contribution

The paper introduces GLSim, a novel method that leverages combined global and local embedding similarities for more accurate hallucination detection in LVLMs.

## Key findings

- GLSim outperforms existing detection methods significantly.
- Benchmark results demonstrate superior accuracy of GLSim.
- The approach is training-free and versatile across scenarios.

## Abstract

Object hallucination in large vision-language models presents a significant challenge to their safe deployment in real-world applications. Recent works have proposed object-level hallucination scores to estimate the likelihood of object hallucination; however, these methods typically adopt either a global or local perspective in isolation, which may limit detection reliability. In this paper, we introduce GLSim, a novel training-free object hallucination detection framework that leverages complementary global and local embedding similarity signals between image and text modalities, enabling more accurate and reliable hallucination detection in diverse scenarios. We comprehensively benchmark existing object hallucination detection methods and demonstrate that GLSim achieves superior detection performance, outperforming competitive baselines by a significant margin.