AINeutralarXiv – CS AI · 3h ago6/10
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Semantic Flow Regularization: Teaching LLMs to Generate Diverse Yet Coherent Responses
Researchers propose Semantic Flow Regularization (SFR), a novel training technique that addresses the problem of large language models generating repetitive, low-diversity responses when fine-tuned for specific styles or personas. SFR uses conditional flow matching to preserve output diversity while maintaining coherence, demonstrating improvements across dialogue systems and code generation tasks without adding inference costs.