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Research probes pretraining vs. retrieval trade-offs in language models

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the interplay between pretraining and retrieval in language models, investigating how scaling model capacity and pretraining data affects retrieval gains. The study found that retrieval benefits are heavily front-loaded, with a significant portion of improvement achieved early in model parameter scaling. Performance gains from retrieval are objective-dependent, with smaller models showing better perplexity and larger models achieving higher accuracy. Notably, retrieval from previously seen data retains most of its effectiveness, suggesting a strategic partitioning of data for internal knowledge and external access in language model design. AI

IMPACT Suggests new strategies for designing language models by optimizing data partitioning between internal knowledge and external retrieval.

RANK_REASON The cluster contains an academic paper detailing research findings on language model interactions. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Research probes pretraining vs. retrieval trade-offs in language models

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The cluster contains an academic paper detailing research findings on language model interactions. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Karan Singh, Michael Yu, Varun Gangal, Zhuofu Tao, Sachin Kumar, Emmy Liu, Steven Y. Feng ·

    To Memorize or to Retrieve: Scaling the Interaction Between Pretraining and Retrieval

    arXiv:2604.00715v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) improves language model (LM) performance by providing relevant context at test time for knowledge-intensive situations. In this work, we systematically study the trade-off between pretr…

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