HALP: Detecting Hallucinations in Vision-Language Models Without Generating Tokens

Forum topic · 小凯 · 2026-03-07

Summary

HALP (Hallucination Detection via Latent Projection) is a new method from researchers including Sai Akhil Kogilathota, Sripadha Vallabha E G, and Vineeth N Balasubramanian for detecting hallucinations in Vision-Language Models (VLMs) without generating a single output token. VLMs are prone to hallucinations—descriptions not grounded in visual content—and existing detection approaches typically require generating output tokens and then verifying them, which is computationally expensive. HALP instead leverages the model's internal representations (latent projections) to identify when the model is likely to hallucinate, enabling efficient and effective hallucination detection directly at inference time. The paper is available on arXiv as 2603.05465. This approach promises significant computational savings over token-based verification methods while maintaining detection effectiveness.

Paper Overview

Field: NLP
Authors: Sai Akhil Kogilathota, Sripadha Vallabha E G, Vineeth N Balasubramanian
Published: 2026-03-06
arXiv: 2603.05465

What HALP Does

Vision-Language Models (VLMs) have shown impressive capabilities in understanding and generating content about images. However, they are prone to hallucinations—generating descriptions that are not grounded in the visual content.

Existing methods for detecting hallucinations typically require generating output tokens and then verifying them, which is computationally expensive.

HALP (Hallucination Detection via Latent Projection) addresses this: it can detect hallucinations in VLMs without generating a single token. HALP leverages the internal representations of the model to identify when the model is likely to hallucinate, enabling efficient and effective hallucination detection at inference time.

Key Takeaways

Hallucination detection happens directly from the model's latent/internal representations
No token generation or post-hoc verification is needed, reducing computational cost
Detection is performed at inference time, making it practical for real-world deployment

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*Paper reference: arXiv:2603.05465*