Toroidal Logit Bias for Hallucination Reduction in Large Language Models
Toroidal Logit Bias for Hallucination Reduction in Large Language Models
Toroidal Logit Bias for Hallucination Reduction in Large Language Models v1.1 - Added TruthfulQA evaluation (817 samples) Key Results: - Custom benchmark (100 prompts): +40% error reduction (Qwen), +15.4% (OLMo) - TruthfulQA (817 prompts): +6.8% error reduction (Qwen) - Paired analysis: 46 improvements vs 32 regressions (McNemar p=0.14) - Consistent directional improvement (b > c) Method: Inference-time toroidal logit bias. No fine-tuning required, ~5% latency overhead. Scope: This work focuses narrowly on an inference-time intervention for hallucination reduction. It makes no claims about ontology, training dynamics, or universal representations. The contribution is operational and empirical. Changelog v1.1: - Added TruthfulQA evaluation (817 samples) with generation-based matching - Added paired McNemar's test analysis - Confirmed directional improvement across both benchmarks
factual accuracy, hallucination reduction, inference-time intervention, large language models, toroidal topology, logit bias
factual accuracy, hallucination reduction, inference-time intervention, large language models, toroidal topology, logit bias
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