BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining
arXiv:2510.06048v4 Announce Type: replace Abstract: Effective data selection is essential for pretraining large language models (LLMs), enhancing efficiency and improving generalization to downstream tasks. However, existing approaches often require leveraging external pretrained models, making it difficult to disentangle the effects of data selection from those of the external pretrained models. In addition, they often overlook the long-term impact of selected data if the model is trained to convergence, primarily due to the prohibitive cost of full-scale LLM pretraining. In this paper, we in
The increasing scale and cost of large language model pretraining are forcing researchers to find more efficient data selection methods that are less reliant on external, potentially biased, models.
This work directly addresses a core challenge in large language model development, impacting training efficiency, model generalization, and potentially reducing the computational resources and specialized external models required.
The ability to pretrain advanced language models with more focused, 'long-term impactful' data and less reliance on external models means more accessible and efficient LLM development.
- · LLM developers
- · AI researchers
- · Cloud providers
- · Inefficient data selection methods
- · High-cost LLM pretraining strategies
More efficient and cost-effective pretraining of large language models becomes possible.
This could lead to a proliferation of more specialized and domain-specific LLMs, reducing barriers to entry for certain applications.
Reduced compute dependency for LLM development could subtly shift the competitive landscape away from exclusive access to extreme compute towards algorithmic efficiency and data curation expertise.
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Read at arXiv cs.LG