A Unified Fix for Vision-Language Hallucinations

Kari Jaaskelainen

24 Nov 2025 — 1 min read

Why this matters

Large vision-language models—the AI that looks at pictures and explains them—still “hallucinate,” stating things not in the image or context.

What’s new

Hallucinations don’t come from one place. They emerge from three interacting routes: image → input text, image → output text, and text → text.
The route that dominates depends on how you ask: multiple-choice (discriminative) vs free-form (generative) questions trigger different pathways.
Intervene-All-Paths finds and tunes “hallucination heads” in each route, aligning with the transformer’s causal structure.

Why it works

By targeting the right heads for each format, the method reduces hallucinations consistently across benchmarks, unifying mitigation across alignment types.

Paper: https://arxiv.org/abs/2511.17254v1

Paper: https://arxiv.org/abs/2511.17254v1

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#AI #ComputerVision #Multimodal #LVLM #Hallucinations #TrustworthyAI #Transformers #MachineLearning #Research

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