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LLM factual recall scales with model size and training data frequency

By PulseAugur Editorial · [1 sources] ·

Researchers have identified a predictable relationship between factual recall in large language models, their size, and the frequency of topics in their training data. By evaluating 38 models on over 8,900 scholarly references, they found that recall quality follows a sigmoid curve based on a combination of model parameters and topic representation. These factors alone accounted for a significant portion of the variance in recall performance across different model families. AI

IMPACT Establishes a new scaling law for factual recall in LLMs, suggesting that performance is predictable based on model size and training data composition.

RANK_REASON Academic paper detailing a new finding about LLM behavior. [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 →

LLM factual recall scales with model size and training data frequency

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Academic paper detailing a new finding about LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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111 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Tegawendé F. Bissyandé ·

    Predictable Confabulations: Factual Recall by LLMs Scales with Model Size and Topic Frequency

    While scaling laws govern aggregate large language model performance, no scaling law has linked factual recall to both model size and training-data composition. We evaluated 38 models on over 8,900 scholarly references evaluated by an automated reference verification system. Reca…

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