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Status: Unchecked ×Keyword: small language models ×Clear all

2 claims from 2 papers

1. Computer Science › Topic Modeling

   It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners

   Schick and Schütze · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · 2021

   The paper reports that small language models, using cloze-style task descriptions and gradient-based training, can match GPT-3's few-shot performance with far fewer parameters.

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   1. UncheckedLanguage models with far fewer parameters than GPT-3, by several orders of magnitude, can reach similar few-shot performance, the paper reports.“We show that performance similar to GPT-3 can be obtained with language models that are much "greener" in that their parameter count is several orders of magnitude smaller.”

2. Computer Science › Natural Language Processing Techniques

   Bigger is not always better: The importance of human-scale language modeling for psycholinguistics

   Wilcox, Hu, Mueller et al. · Journal of Memory and Language · 2025

   The paper argues for human-scale language modelling in psycholinguistics and reports on the first BabyLM Challenge, a shared task for training language models on 100 million words or less.

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   1. UncheckedModels trained on 100 million words or fewer, a human-scale amount, learned robust linguistic generalizations, though not yet by cognitively plausible mechanisms.“For cognitive scientists, the challenge demonstrated that robust linguistic generalizations can be learned by models trained on a human-scale dataset, though this is not yet achieved through cognitively plausible mechanisms.”