Small Language Models Like Small Vocabularies: Probing the Linguistic Abilities of Grapheme- and Phoneme-Based Baby Llamas

• URL: http://arxiv.org/abs/2410.01487v1
• Date: Wed, 2 Oct 2024 12:36:08 GMT
• Title: Small Language Models Like Small Vocabularies: Probing the Linguistic Abilities of Grapheme- and Phoneme-Based Baby Llamas
• Authors: Bastian Bunzeck, Daniel Duran, Leonie Schade, Sina Zarrieß,
• Abstract summary: We show that small models based on the Llama architecture can achieve strong linguistic performance on standard syntactic and novel lexical/phonetic benchmarks. Our findings suggest a promising direction for creating more linguistically plausible language models that are better suited for computational studies of language acquisition and processing.
• Score: 7.585433383340306
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Current language models use subword-based tokenization algorithms like Byte Pair Encoding, which put their validity as models of linguistic representations into question. In this paper, we explore the potential of tokenization-free, phoneme- and grapheme-based language models. We demonstrate that small models based on the Llama architecture can achieve strong linguistic performance on standard syntactic and novel lexical/phonetic benchmarks when trained with character-level vocabularies. We further show that phoneme-based models without any graphemic biases almost match grapheme-based models in standard tasks and novel evaluations. Our findings suggest a promising direction for creating more linguistically plausible language models that are better suited for computational studies of language acquisition and processing.

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