From Parameters to Data: A Task-Parameter-Guided Fine-Tuning Pipeline for Efficient LLM Alignment
arXiv:2605.21558v1 Announce Type: new Abstract: Adapting Large Language Models (LLMs) to specialized domains typically incurs high data and computational overhead. While prior efficiency efforts have largely treated data selection and parameter-efficient fine-tuning as isolated processes, our empirical analysis suggests they may be intrinsically coupled. We posit the Strong Map Hypothesis: a sparse subset of attention heads plays a dominant role in task-specific adaptation, acting as keys that unlock specific data patterns. Building on this observation, we propose From Parameters to Data (P2D)
The increasing computational and data demands of large language models necessitate more efficient alignment techniques to broaden their accessibility and applicability across specialized domains.
This research outlines a method to significantly reduce the overhead of adapting LLMs, potentially democratizing access to highly performant AI for niche applications and smaller entities.
The proposed P2D pipeline shifts the paradigm for LLM fine-tuning by intrinsically linking data selection and parameter-efficient tuning, leading to more efficient and targeted model adaptation.
- · AI developers
- · Specialized industries adopting AI
- · High-compute-cost LLM fine-tuning services
Reduced costs and time for fine-tuning LLMs for specific tasks.
Increased adoption of LLMs in specialized, data-scarce domains due to lower barriers to entry.
Acceleration of AI agent development within niche sectors, leading to a broader range of autonomous applications.
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Read at arXiv cs.LG