Tokens, the oft-overlooked appetizer: Large language models, the distributional hypothesis, and meaning

• URL: http://arxiv.org/abs/2412.10924v4
• Date: Sun, 13 Apr 2025 16:17:45 GMT
• Title: Tokens, the oft-overlooked appetizer: Large language models, the distributional hypothesis, and meaning
• Authors: Julia Witte Zimmerman, Denis Hudon, Kathryn Cramer, Alejandro J. Ruiz, Calla Beauregard, Ashley Fehr, Mikaela Irene Fudolig, Bradford Demarest, Yoshi Meke Bird, Milo Z. Trujillo, Christopher M. Danforth, Peter Sheridan Dodds,
• Abstract summary: Tokenization is a necessary component within the current architecture of many language models.<n>We discuss how tokens and pretraining can act as a backdoor for bias and other unwanted content.<n>We relay evidence that the tokenization algorithm's objective function impacts the large language model's cognition.
• Score: 31.632816425798108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tokenization is a necessary component within the current architecture of many language models, including the transformer-based large language models (LLMs) of Generative AI, yet its impact on the model's cognition is often overlooked. We argue that LLMs demonstrate that the Distributional Hypothesis (DH) is sufficient for reasonably human-like language performance, and that the emergence of human-meaningful linguistic units among tokens and current structural constraints motivate changes to existing, linguistically-agnostic tokenization techniques, particularly with respect to their roles as (1) semantic primitives and as (2) vehicles for conveying salient distributional patterns from human language to the model. We explore tokenizations from a BPE tokenizer; extant model vocabularies obtained from Hugging Face and tiktoken; and the information in exemplar token vectors as they move through the layers of a RoBERTa (large) model. Besides creating sub-optimal semantic building blocks and obscuring the model's access to the necessary distributional patterns, we describe how tokens and pretraining can act as a backdoor for bias and other unwanted content, which current alignment practices may not remediate. Additionally, we relay evidence that the tokenization algorithm's objective function impacts the LLM's cognition, despite being arguably meaningfully insulated from the main system intelligence. [First uploaded to arXiv in December, 2024.]

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