[Paper] HalluShift++: Bridging Language and Vision through Internal Representation Shifts for Hierarchical Hallucinations in MLLMs

Source: arXiv - 2512.07687v1

Overview

The paper “HalluShift++: Bridging Language and Vision through Internal Representation Shifts for Hierarchical Hallucinations in MLLMs” tackles a pressing problem in multimodal large language models (MLLMs): hallucinations—outputs that sound plausible but contradict what’s actually shown in the image. Instead of relying on external language models to spot these errors, the authors argue that the models’ own internal activations contain tell‑tale signs of hallucination. By detecting and interpreting these “representation shifts,” they extend a previously text‑only detection method (HalluShift) to the multimodal domain.

Key Contributions

• Internal‑signal hypothesis: Demonstrates that hallucinations manifest as measurable irregularities in the layer‑wise activations of MLLMs, not just as distributional drifts.
• HalluShift++ framework: Extends the original HalluShift method to multimodal settings, introducing hierarchical analysis across vision‑language fusion layers.
• Domain‑agnostic detection: Provides a hallucination detector that does not depend on an external LLM evaluator, reducing cascade errors and improving adaptability to niche visual domains.
• Open‑source implementation: Releases a full codebase (https://github.com/C0mRD/HalluShift_Plus) for reproducibility and community extension.
• Comprehensive evaluation: Benchmarks HalluShift++ on several MLLM architectures (e.g., BLIP‑2, LLaVA) and datasets, showing superior precision/recall over prior external‑LLM baselines.

Methodology

1. Layer‑wise activation extraction: For a given image‑text pair, the model’s hidden states are captured at multiple stages—visual encoder, cross‑modal fusion, and language decoder layers.
2. Shift quantification: The authors compute a representation shift score by measuring deviations from a “clean” reference distribution (obtained from correctly aligned image‑caption pairs) using metrics such as KL‑divergence and cosine distance.
3. Hierarchical aggregation: Scores from early visual layers, mid‑fusion layers, and late language layers are combined with learned weights, reflecting the intuition that hallucinations can arise at any stage of processing.
4. Thresholding & classification: A calibrated threshold turns the aggregated shift score into a binary hallucination flag (or a confidence‑scaled probability).
5. Training‑free operation: The detector works out‑of‑the‑box; no fine‑tuning of the underlying MLLM is required, making it lightweight for developers.

Results & Findings

• Detection accuracy: HalluShift++ achieves ≈85% F1 on a curated hallucination benchmark, outperforming the best external‑LLM evaluator (≈73% F1).
• Layer importance: Ablation studies reveal that mid‑fusion layers contribute the most signal (≈40% of total importance), confirming that hallucinations often emerge during vision‑language integration.
• Robustness across models: The method generalizes well to different MLLM backbones (BLIP‑2, LLaVA, MiniGPT‑4) without re‑training, indicating that the internal shift phenomenon is model‑agnostic.
• Speed: Since only forward passes are needed, detection adds ≈15 ms per query on a single RTX 3080, suitable for real‑time pipelines.

Practical Implications

• Safer AI assistants: Developers can embed HalluShift++ into chat‑or‑image assistants (e.g., visual QA bots) to flag or suppress hallucinated responses before they reach end‑users.
• Content moderation: Automated pipelines for image captioning on social platforms can use the detector to catch factually incorrect descriptions that might mislead or violate policy.
• Domain‑specific deployment: Because the method does not rely on a generic LLM, it can be applied to specialized domains (medical imaging, satellite imagery) where external LLMs lack expertise.
• Debugging tool: Model engineers can visualize which layers exhibit the strongest shifts, helping pinpoint architectural bottlenecks or training data gaps that cause hallucinations.
• Cost reduction: Eliminating the need for a secondary LLM evaluator cuts inference cost and latency, especially important for edge or mobile deployments.

Limitations & Future Work

• Reference distribution dependence: The detector requires a clean reference set of image‑caption pairs; constructing this set for highly specialized domains can be non‑trivial.
• Threshold sensitivity: Selecting an optimal shift‑score threshold may need task‑specific calibration; a one‑size‑fits‑all threshold can lead to false positives/negatives.
• Scope of hallucination types: The current formulation focuses on factual mismatches; more subtle semantic drifts (e.g., style or tone inconsistencies) are not explicitly captured.
• Future directions: The authors suggest extending HalluShift++ to multimodal generation beyond captioning (e.g., visual storytelling), integrating adaptive thresholding via reinforcement learning, and exploring self‑supervised refinement of the reference distribution.

Authors

• Sujoy Nath
• Arkaprabha Basu
• Sharanya Dasgupta
• Swagatam Das

Paper Information

• arXiv ID: 2512.07687v1
• Categories: cs.CL, cs.CV
• Published: December 8, 2025
• PDF: Download PDF