VIB-Probe: Detecting and Mitigating Hallucinations in Vision-Language Models via Variational Information Bottleneck

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, long

Summary

VIB-Probe is a novel framework designed to detect and mitigate hallucinations in Vision-Language Models (VLMs) by analyzing their internal attention mechanisms. Unlike existing methods that rely on output logits or external tools, VIB-Probe investigates the outputs of internal attention heads, postulating that specific heads carry primary signals for truthful generation. It leverages Variational Information Bottleneck (VIB) theory to extract discriminative patterns across layers and heads, filtering out semantic nuisances. The framework also introduces an inference-time intervention strategy, using gradients from the VIB probe to identify and suppress hallucination-sensitive attention heads during decoding. Extensive experiments across diverse benchmarks demonstrate that VIB-Probe significantly outperforms existing baselines in both hallucination detection and mitigation across various VLM architectures, including MiniGPT-4, LLaVA-v1.5-7B, LLaVA-v1.6-Mistral-7B, and Qwen2.5-VL-7B-Instruct.

Key takeaway

For research scientists and engineers developing or deploying Vision-Language Models, VIB-Probe offers a robust, internal mechanism to address VLM hallucinations. You should consider integrating this Variational Information Bottleneck-based approach to not only detect but also actively mitigate unfaithful generations. This method provides a training-free, inference-time intervention, enhancing model reliability without costly retraining.

Key insights

VIB-Probe uses Variational Information Bottleneck theory to detect and mitigate VLM hallucinations by analyzing internal attention head outputs.

Principles

• Hallucination signals are encoded in specific attention heads.
• Information Bottleneck theory can distill predictive signals from noise.
• Gradient-based attribution identifies causally influential heads.

Method

VIB-Probe extracts raw attention head outputs, feeds them into an IB Encoder for a compact latent representation, and uses a linear classifier for hallucination risk. Mitigation involves gradient-based attribution to identify and suppress sensitive attention heads during inference.

In practice

• Apply VIB-Probe for robust VLM hallucination detection.
• Implement inference-time head suppression for mitigation.
• Utilize gradient-based attribution to pinpoint problematic attention heads.

Topics

• Vision-Language Models
• Hallucination Detection
• Hallucination Mitigation
• Variational Information Bottleneck
• Attention Heads

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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• Weakly Supervised Distillation of Hallucination Signals into Transformer Representations — Hallucination signals can be distilled into LLM internal representations for detection without external inference-time resources.
• Mechanisms of Prompt-Induced Hallucination in Vision-Language Models — VLMs hallucinate by prioritizing text over vision, a behavior traceable to specific, ablatable attention heads.
• How Many Counterfactuals Does It Take? Probing VLM Hallucinations Through Circuits and Causal Effects — The study quantifies VLM hallucination robustness using a causal influence metric and circuit discovery.
• OpenHalDet: A Unified Benchmark for Hallucination Detection across Diverse Generation Scenarios — Standardized benchmarks are crucial for reproducible and comparable LLM hallucination detection research.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.