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Adaptive Activation Cancellation for Hallucination Mitigation in Large Language Models

arXiv:2603.10195v1 Announce Type: new Abstract: Large Language Models frequently generate fluent but factually incorrect text. We propose Adaptive Activation Cancellation (AAC), a real-time inference-time framework that treats hallucination-associated neural activations as structured interference within the transformer residual stream, drawing an explicit analogy to classical adaptive noise cancellation from signal processing. The framework identifies Hallucination Nodes (H-Nodes) via layer-wise linear probing and suppresses them using a confidence-weighted forward hook during auto-regressive generation -- requiring no external knowledge, no fine-tuning, and no additional inference passes. Evaluated across OPT-125M, Phi-3-mini, and LLaMA 3-8B on TruthfulQA and HaluEval, the real-time hook is the only intervention that consistently improves downstream accuracy on all three scales. Critically, the method is strictly surgical: WikiText-103 perplexity and MMLU reasoning accuracy are prese

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Eric Yocam, Varghese Vaidyan, Gurcan Comert, Paris Kalathas, Yong Wang, Judith L. Mwakalonge · · 1 min read · 12 views
#cs.CL #cs.AI #cs.LG

arXiv:2603.10195v1 Announce Type: new Abstract: Large Language Models frequently generate fluent but factually incorrect text. We propose Adaptive Activation Cancellation (AAC), a real-time inference-time framework that treats hallucination-associated neural activations as structured interference within the transformer residual stream, drawing an explicit analogy to classical adaptive noise cancellation from signal processing. The framework identifies Hallucination Nodes (H-Nodes) via layer-wise linear probing and suppresses them using a confidence-weighted forward hook during auto-regressive generation -- requiring no external knowledge, no fine-tuning, and no additional inference passes. Evaluated across OPT-125M, Phi-3-mini, and LLaMA 3-8B on TruthfulQA and HaluEval, the real-time hook is the only intervention that consistently improves downstream accuracy on all three scales. Critically, the method is strictly surgical: WikiText-103 perplexity and MMLU reasoning accuracy are preserved at exactly 0.0% degradation across all three model scales, a property that distinguishes AAC from interventions that trade fluency or general capability for factual improvement. On the LLaMA 3-8B scale, the hook additionally yields positive generation-level gains (MC1 +0.04; MC2 +0.003; Token-F1 +0.003) while achieving probe-space selectivity 5.94x - 3.5x higher than the ITI baseline -- demonstrating that targeted neuron-level suppression can simultaneously improve factual accuracy and preserve model capability.

Executive Summary

This article proposes Adaptive Activation Cancellation (AAC), a real-time inference-time framework to mitigate hallucination in large language models. AAC identifies and suppresses hallucination-associated neural activations, improving factual accuracy without degrading model capability. Evaluated across three model scales, AAC consistently improves downstream accuracy while preserving fluency and general capability.

Key Points

  • ▸ Adaptive Activation Cancellation (AAC) framework for hallucination mitigation
  • ▸ Identification of Hallucination Nodes (H-Nodes) via layer-wise linear probing
  • ▸ Suppression of H-Nodes using confidence-weighted forward hook during auto-regressive generation

Merits

Effective Hallucination Mitigation

AAC consistently improves downstream accuracy on all three model scales without degrading model capability.

Surgical Intervention

AAC is a strictly surgical method that preserves WikiText-103 perplexity and MMLU reasoning accuracy at exactly 0.0% degradation.

Demerits

Limited Evaluation

The evaluation of AAC is limited to three model scales and two datasets, which may not be representative of all large language models and applications.

Expert Commentary

The proposed AAC framework offers a promising approach to mitigating hallucination in large language models. The ability to identify and suppress hallucination-associated neural activations in real-time can significantly improve the factual accuracy of language models. However, further evaluation and refinement of AAC are necessary to ensure its effectiveness and applicability across various models and applications. The implications of AAC extend beyond the technical domain, as it can inform policies and regulations related to the use of language models in high-stakes applications.

Recommendations

  • ✓ Further evaluation of AAC across a wider range of models and applications
  • ✓ Investigation of the potential applications of AAC in other areas of artificial intelligence and machine learning

Sources

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