Small Language Models Can Learn Linguistic Representations from Character-Level Inputs

핵심 개념

Small language models trained on character-level inputs can capture linguistic structures at various levels, including syntax, lexicon, and phonetics, performing comparably to or even outperforming larger subword-based models.

초록

The study explores the potential of tokenization-free, character-based language models, including both grapheme-based and phoneme-based models. The key findings are:

Character-level language models perform as well as or better than larger subword-based models on a variety of linguistic evaluations, including syntactic, lexical, and phonetic tasks.

Grapheme-based models generally outperform phoneme-based models, but the differences are less pronounced for more phonetically-oriented tasks. This suggests that grapheme-based models may pick up more structural biases from orthography than commonly assumed.

Removing word boundaries (whitespace) from the input data can have varying effects, improving performance on lexical/phonological tasks but decreasing performance on syntactic evaluations.

The results challenge the assumption that grapheme-based models are completely tabula rasa and suggest that character-level models can provide valuable insights into the representation and learning of linguistic knowledge at different levels.

통계

Our small (15M parameter) character-based models perform comparably to or better than a larger (58M parameter) subword-based model on standard syntactic evaluations.
Grapheme-based models achieve near-perfect performance (99%) on a lexical decision task, outperforming the subword-based model.
Phoneme-based models perform reasonably well (63-68%) on the lexical decision task, despite their smaller vocabulary size.
Both grapheme and phoneme models perform well above chance on rhyme prediction (78-92%) and age prediction (58-61%) tasks.

인용구

"The systematic superiority of grapheme- over phoneme-based models calls the commonly assumed tabula rasa-ness of grapheme-based models into question."
"Explaining these effects requires further research and we believe the following directions to be worth exploring: (i) grapheme-based models may pick up all kinds of inductive biases introduced by orthography, (ii) phoneme-based models may suffer from errors introduced through too rigid G2P."

핵심 통찰 요약

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

by Bast... 게시일 arxiv.org 10-03-2024

https://arxiv.org/pdf/2410.01487.pdf

더 깊은 질문

What other linguistic phenomena, beyond the ones explored in this study, could character-based models shed light on?

Character-based models have the potential to illuminate a variety of linguistic phenomena that extend beyond the syntactic, lexical, and phonetic tasks explored in this study. For instance, they could provide insights into morphological processes, such as inflection and derivation, by analyzing how these models learn to segment and represent morphemes from unsegmented input. Additionally, character-based models could be employed to investigate prosodic features of language, such as intonation and stress patterns, which are crucial for understanding meaning in spoken language. By training on phonetically-rich data, these models could also explore speech disfluencies (e.g., hesitations, repetitions) and their impact on language processing. Furthermore, they could be used to study code-switching phenomena in bilingual speakers, examining how character-level representations adapt to shifts between languages. Overall, character-based models could serve as a powerful tool for probing the intricacies of language acquisition and processing across various linguistic levels.

How would the performance of character-based models be affected by using more naturalistic, phonetically-rich speech data instead of orthographic text?

The performance of character-based models would likely improve significantly if trained on more naturalistic, phonetically-rich speech data rather than traditional orthographic text. Naturalistic speech data captures the variability and nuances of spoken language, including intonation, stress, and coarticulation, which are often lost in written forms. By incorporating this richness, character-based models could learn more accurate representations of phonetic units, leading to enhanced performance on tasks related to phonological awareness and speech perception. Moreover, exposure to diverse speech patterns and dialects would enable these models to better generalize across different linguistic contexts, improving their robustness and adaptability. This shift could also facilitate the exploration of sociolinguistic variation, allowing models to account for differences in pronunciation and usage among various speaker groups. Ultimately, training on phonetically-rich data would provide character-based models with a more comprehensive understanding of language as it is naturally used, potentially leading to breakthroughs in language processing and acquisition research.

How do the learned representations in character-based models differ from subword-based models, and what insights could this provide into the cognitive processes underlying language acquisition and processing?

The learned representations in character-based models differ fundamentally from those in subword-based models in terms of granularity and linguistic fidelity. Character-based models operate at the level of individual characters, allowing them to capture the morphological and phonological structure of language without the biases introduced by subword tokenization. This enables them to learn more nuanced representations of linguistic units, such as phonemes and morphemes, which are essential for understanding the building blocks of language. In contrast, subword-based models may overlook these finer distinctions, as they aggregate characters into larger units that may not correspond to meaningful linguistic segments.
These differences in representation can provide valuable insights into the cognitive processes underlying language acquisition and processing. For instance, character-based models may better reflect the bottom-up processing strategies employed by children as they learn to segment speech into meaningful units. This aligns with theories of language acquisition that emphasize the importance of exposure to raw linguistic input in developing phonological and morphological awareness. Additionally, the ability of character-based models to learn from unsegmented input can shed light on the tabula rasa nature of early language learners, suggesting that linguistic knowledge is built incrementally from the ground up rather than relying on pre-defined structures.
In summary, the distinct representations learned by character-based models not only enhance their performance on various linguistic tasks but also offer a deeper understanding of the cognitive mechanisms involved in language learning and processing, paving the way for future research in this area.