AI RESEARCH
Diversity in Large Language Models under Supervised Fine-Tuning
arXiv CS.LG
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ArXi:2605.00195v1 Announce Type: new Supervised Fine-Tuning (SFT) is essential for aligning Large Language Models (LLMs) with user intent, yet it is believed to suppress generative diversity. Although this reduction is frequently referenced, formal empirical testing of the phenomenon remains limited. The expressiveness of LLMs by itself was addressed by multiple prior methods. Their varying perspectives suggest that deeper analysis could yield further improvements.