MechaRule localizes agonist neurons in LLMs via contrastive hierarchical ablation to ground rule extraction in circuitry, recalling 96.8% of high-effect neurons and reducing task performance when suppressed.
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LLMs exhibit mid-layer representation advantage for recommendations; MARC compresses representations modularly to reduce costs while improving performance, as shown in a large-scale online advertising deployment.
MESA reduces hallucinations in LVLMs via controlled selective latent intervention that preserves the original token distribution.
citing papers explorer
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Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation
MechaRule localizes agonist neurons in LLMs via contrastive hierarchical ablation to ground rule extraction in circuitry, recalling 96.8% of high-effect neurons and reducing task performance when suppressed.
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Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations
LLMs exhibit mid-layer representation advantage for recommendations; MARC compresses representations modularly to reduce costs while improving performance, as shown in a large-scale online advertising deployment.
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Mitigating Entangled Steering in Large Vision-Language Models for Hallucination Reduction
MESA reduces hallucinations in LVLMs via controlled selective latent intervention that preserves the original token distribution.