Analyzing Similarity Metrics for Data Selection for Language Model Pretraining

Description

The provided text, primarily an excerpt from a research paper, centers on the critical task of measuring similarity metrics for data selection during the pretraining of large language models (LLMs). The authors introduce a novel evaluation framework to assess how well different text embedding models are suited for curating high-quality and diverse datasets, arguing that generic, off-the-shelf embeddings often underperform compared to specialized methods. The framework uses three criteria: correlation with pretraining loss, utility in a diversity-based data curation strategy to measure downstream task performance, and cluster purity with respect to underlying data sources. Experiments on the Pile dataset demonstrate that simple, specialized embeddings—even those requiring no forward pass—can match or exceed the performance of more complex, general-purpose models, underscoring the need for domain-specific similarity metrics. #google paper - https://arxiv.org/abs/2502.02494 subscribe - https://t.me/arxivpaper donations: USDT: 0xAA7B976c6A9A7ccC97A3B55B7fb353b6Cc8D1ef7 BTC: bc1q8972egrt38f5ye5klv3yye0996k2jjsz2zthpr ETH: 0xAA7B976c6A9A7ccC97A3B55B7fb353b6Cc8D1ef7 SOL: DXnz1nd6oVm7evDJk25Z2wFSstEH8mcA1dzWDCVjUj9e created with NotebookLM