AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation

TL;DR

AMBER is a novel, low-cost, multi-dimensional benchmark designed to evaluate hallucinations in Multi-modal Large Language Models without relying on large language models, covering various hallucination types and tasks.

Contribution

The paper introduces AMBER, an LLM-free, multi-dimensional benchmark for evaluating hallucinations in MLLMs, addressing high evaluation costs and limited dimensions of previous methods.

Findings

Mainstream MLLMs exhibit significant hallucinations.

AMBER effectively evaluates hallucinations across multiple dimensions.

Guidelines are provided for reducing hallucinations in MLLMs.

Abstract

Despite making significant progress in multi-modal tasks, current Multi-modal Large Language Models (MLLMs) encounter the significant challenge of hallucinations, which may lead to harmful consequences. Therefore, evaluating MLLMs' hallucinations is becoming increasingly important in model improvement and practical application deployment. Previous works are limited in high evaluation costs (e.g., relying on humans or advanced LLMs) and insufficient evaluation dimensions (e.g., types of tasks and hallucinations). In this paper, we propose an LLM-free multi-dimensional benchmark AMBER, which can be used to evaluate both generative task and discriminative task including existence, attribute and relation hallucination. Based on AMBER, we design a low-cost and efficient evaluation pipeline. Additionally, we conduct a comprehensive evaluation and detailed analysis of mainstream MLLMs including GPT-4V(ision), and also give guideline suggestions for mitigating hallucinations. The data and code of AMBER are available at https://github.com/junyangwang0410/AMBER.