Explore the Hallucination on Low-level Perception for MLLMs

TL;DR

This paper introduces QL-Bench, a benchmark for evaluating self-awareness in Multi-modality Large Language Models (MLLMs) regarding low-level visual perception, revealing current limitations and potential areas for improvement.

Contribution

The paper presents QL-Bench and the LLSAVisionQA dataset to assess self-awareness in MLLMs' low-level perception, highlighting the models' strengths and weaknesses.

Findings

Some models show strong low-level visual capabilities.

Simpler questions are answered more accurately than complex ones.

Self-awareness improves with more challenging questions.

Abstract

The rapid development of Multi-modality Large Language Models (MLLMs) has significantly influenced various aspects of industry and daily life, showcasing impressive capabilities in visual perception and understanding. However, these models also exhibit hallucinations, which limit their reliability as AI systems, especially in tasks involving low-level visual perception and understanding. We believe that hallucinations stem from a lack of explicit self-awareness in these models, which directly impacts their overall performance. In this paper, we aim to define and evaluate the self-awareness of MLLMs in low-level visual perception and understanding tasks. To this end, we present QL-Bench, a benchmark settings to simulate human responses to low-level vision, investigating self-awareness in low-level visual perception through visual question answering related to low-level attributes such as clarity and lighting. Specifically, we construct the LLSAVisionQA dataset, comprising 2,990 single images and 1,999 image pairs, each accompanied by an open-ended question about its low-level features. Through the evaluation of 15 MLLMs, we demonstrate that while some models exhibit robust low-level visual capabilities, their self-awareness remains relatively underdeveloped. Notably, for the same model, simpler questions are often answered more accurately than complex ones. However, self-awareness appears to improve when addressing more challenging questions. We hope that our benchmark will motivate further research, particularly focused on enhancing the self-awareness of MLLMs in tasks involving low-level visual perception and understanding.