arXiv:2605.30844v1 Announce Type: cross Abstract: Fine-tuning is often believed to reduce uncertainty and diversity in large language models, but existing analyses overlook output length, a key confounder, and therefore fail to capture how uncertainty is distributed across an entire generation rollout. To address this, we propose Canopy Entropy ($\mathrm{CE}^\star$), a measure that views language generation from a tree perspective, where ``canopy'' represents the space of all possible rollouts, making $\mathrm{CE}^\star$ naturally quantify the effective size of the generation space. $\mathrm{C
The paper addresses a critical, ongoing debate regarding the impact of fine-tuning on LLM output diversity and information content, offering a new metric to accurately assess this phenomenon.
Improving understanding of how fine-tuning affects LLM output allows for more effective model development and application, crucial for trust and reliability in AI systems.
The proposed Canopy Entropy metric provides a more nuanced way to measure information conveyance in LLMs, potentially leading to more targeted fine-tuning strategies that balance specificity with informativeness.
- · AI researchers
- · LLM developers
- · Enterprises deploying AI agents
- · Developers using suboptimal fine-tuning methods
- · Users relying on less informative LLM outputs
Researchers gain a better tool to evaluate and optimize fine-tuning strategies for language models.
This improved understanding leads to the development of more robust and reliable AI agents and applications.
More effective fine-tuning reduces computational waste and improves the societal utility of large language models.
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Read at arXiv cs.AI