LLM hallucination paper(using math) accepted to ICML workshop[R]

Short summary

SRM-LoRA is a sub-Riemannian-inspired LoRA method that reduces LLM hallucination by reshaping backward gradients using a sensitivity-based Riemannian metric. The metric suppresses high-cost update directions without changing inference cost. Trained only on HaluEval-QA, it improves factual reliability on both in-distribution and out-of-distribution benchmarks, and was accepted to the ICML 2026 FoGen workshop.

• •SRM-LoRA uses a sensitivity-based Riemannian metric to reshape LoRA gradient updates
• •Suppresses high-cost update directions to reduce hallucination without added inference cost
• •Validated on HaluEval-QA with generalization to out-of-distribution benchmarks; accepted at ICML 2026 FoGen workshop

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