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Adaptive Activation Cancellation for Hallucination Mitigation in Large Language Models
arXiv – CS AI|Eric Yocam, Varghese Vaidyan, Gurcan Comert, Paris Kalathas, Yong Wang, Judith L. Mwakalonge|
🤖AI Summary
Researchers developed Adaptive Activation Cancellation (AAC), a real-time framework that reduces hallucinations in large language models by identifying and suppressing problematic neural activations during inference. The method requires no fine-tuning or external knowledge and preserves model capabilities while improving factual accuracy across multiple model scales including LLaMA 3-8B.
Key Takeaways
- →AAC treats hallucination-associated neural activations as structured interference and suppresses them using confidence-weighted forward hooks during generation.
- →The method requires no external knowledge, fine-tuning, or additional inference passes, making it practically deployable.
- →Testing across OPT-125M, Phi-3-mini, and LLaMA 3-8B showed consistent accuracy improvements on TruthfulQA and HaluEval benchmarks.
- →The framework preserves model capabilities with exactly 0.0% degradation on WikiText-103 perplexity and MMLU reasoning tasks.
- →AAC achieves 5.94x to 3.5x higher selectivity than baseline methods while simultaneously improving factual accuracy and preserving general capabilities.
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#large-language-models#hallucination-mitigation#adaptive-activation-cancellation#inference-optimization#neural-networks#ai-safety#llama#truthfulqa#model-accuracy
Read Original →via arXiv – CS AI
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