arXiv:2606.27242v1 Announce Type: new Abstract: Training-free source selection for LLM families with shared vocabularies arises in scientific string domains such as SMILES, protein, and genomic sequences, where candidate corpora share a tokenizer but differ in prediction targets. This creates an activation-dark regime: representation-similarity metrics can be uninformative without assumptions about label-conditioned error geometry, while classical update-geometry metrics are computationally prohibitive at vocabulary scale. We show that, in a shared-output head setting, representation metrics (
The paper addresses a current challenge in LLM development regarding efficient training-free source selection, prompted by the increasing complexity and specialized applications of large language models.
This research provides a novel computational method for assessing data utility for LLMs, potentially leading to more targeted and efficient model development, especially for scientific and domain-specific applications.
The ability to more accurately and efficiently assess data's relevance without full training will improve LLM adaptability and reduce resource expenditure in specialized domains, shifting focus towards data selection strategies.
- · AI researchers
- · Biotech companies
- · Pharmaceutical industry
- · LLM developers
- · Companies with inefficient data selection processes
Improved efficiency in training specialized LLMs for scientific and technical fields.
Faster development and deployment of new AI applications in sectors like drug discovery and materials science.
Enhanced scientific discovery through more accurate and tailored AI models, potentially accelerating research timelines and innovation.
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