Exploring Causes and Mitigation of Hallucinations in Large Vision Language Models (2502.16842v1)

Abstract: Large Vision-LLMs (LVLMs) integrate image encoders with LLMs to process multi-modal inputs and perform complex visual tasks. However, they often generate hallucinations by describing non-existent objects or attributes, compromising their reliability. This study analyzes hallucination patterns in image captioning, showing that not all tokens in the generation process are influenced by image input and that image dependency can serve as a useful signal for hallucination detection. To address this, we develop an automated pipeline to identify hallucinated objects and train a token-level classifier using hidden representations from parallel inference passes-with and without image input. Leveraging this classifier, we introduce a decoding strategy that effectively controls hallucination rates in image captioning at inference time.

Analysis of Hallucination Mitigation in Large Vision LLMs

The paper "Exploring Causes and Mitigation of Hallucinations in Large Vision LLMs" provides an in-depth paper on the hallucination problem in Large Vision-LLMs (LVLMs), particularly in the task of image captioning. The research focuses on understanding the patterns and root causes of hallucinations, defined as instances where generated descriptions mismatch the visual inputs by including non-existent objects or attributes. With LVLMs playing an increasingly significant role in multi-modal artificial intelligence applications, mitigating hallucinations is crucial for enhancing their reliability and performance.

The authors begin by highlighting the fundamental challenge that while LVLMs have demonstrated substantial proficiency in multi-modal tasks, their tendency to hallucinate limits practical applicability. This propensity for hallucination is attributed to the model's reliance on language priors over time, overshadowing the image input information. Such behavior is particularly evident in later parts of generated sequences and can be exacerbated by the fixed response patterns the models learn during fine-tuning on synthetic training data, which reinforces language-driven outputs.

The research makes notable contributions to both understanding and addressing this issue:

1. Automated Pipeline for Annotation: The authors have developed an efficient automated pipeline that leverages multiple open-vocabulary object detection tools to identify hallucinated objects in image captions. This pipeline circumvents the need for costly manual annotation and instead creates a scaled framework that can label hallucinations in generated text.
2. Token-Level Classifier Development: Using hidden representations from inference passes with and without image input, the paper proposes a token-level hallucination classifier. This classifier can predict whether parts of the generated text are hallucinated based on the model's dependence on visual input, as indicated by hidden state divergence.
3. Novel Decoding Strategy: A pivotal innovation is the introduction of a decoding strategy that integrates the classifier's evaluations with sampling techniques to control the hallucination rate during the generation process. This method focuses on both identifying inaccurate tokens and refining token selection to ensure more grounded and reliable captions.

These methodological advances are rigorously tested against various benchmarks and show a marked improvement in reducing hallucinations without sacrificing descriptive richness. Notably, this approach allows for dynamic adjustment of hallucination rates by modulating the classifier's influence during decoding.

The paper acknowledges that although language priors play a significant role in LVLMs' generative capabilities, they pose a risk when not adequately balanced with visual input features. The underlying suggestion is to enhance architecture and training paradigms to preserve visual information throughout the generation process more effectively. This insight opens avenues for future research, indicating the potential of developing more sophisticated integration techniques within models to better harness multi-modal data.

In conclusion, the paper's robust methodological framework offers a scalable, efficient means to address a core challenge in modern LVLMs. Beyond practical applications, this research contributes to the theoretical understanding of multi-modal learning dynamics, suggesting that the intricate balance between language and visual cues needs further exploration to optimize LVLMs for diverse real-world scenarios.