Tokenization Falling Short: On Subword Robustness in Large Language Models (2406.11687v3)

Abstract: LLMs typically tokenize raw text into sequences of subword identifiers from a predefined vocabulary, a process inherently sensitive to typographical errors, length variations, and largely oblivious to the internal structure of tokens--issues we term the curse of tokenization. In this study, we delve into these drawbacks and demonstrate that LLMs remain susceptible to these problems. This study systematically investigates these challenges and their impact on LLMs through three critical research questions: (1) complex problem solving, (2) token structure probing, and (3) resilience to typographical variation. Our findings reveal that scaling model parameters can mitigate the issue of tokenization; however, LLMs still suffer from biases induced by typos and other text format variations. Our experiments show that subword regularization such as BPE-dropout can mitigate this issue. We release our evaluation code and data at https://github.com/FloatAI/TKEval.

An Expert Analysis of "The Curse of Tokenization: An In-Depth Exploration"

In the current landscape of natural language processing, tokenization plays a pivotal role in preparing data for LLMs. The paper "Tokenization Falling Short: The Curse of Tokenization" by Yekun Chai et al. critically examines the fundamental limitations inherent in this process and its implications for the efficacy and robustness of LLMs. The authors identify these limitations as the "curse of tokenization," which primarily includes sensitivity to typographical errors, length unawareness, and obliviousness to the internal structure of tokens. This analysis explores these challenges, investigates their effects on LLMs, and evaluates potential mitigation strategies.

Key Findings and Analysis

The paper articulates three major research questions (RQs) related to the tokenization challenges in LLMs: complex problem solving, token structure probing, and resilience to typographical variation. Through these RQs, the authors systematically assess the impact of tokenization on various models, delivering several intriguing findings.

1. Complex Problem Solving: The authors employ anagram and mathematical language comprehension tasks to assess LLMs' performance under complex problem-solving conditions. Notably, larger models such as Llama3-70B demonstrate superior performance on these tasks, particularly when dealing with LaTeX-formatted content. However, sensitivity to typographical variations remains, underscoring the vulnerability of even state-of-the-art models to tokenization pitfalls.
2. Token Structure Probe: In exploring intra- and inter-token probing tasks, the paper reveals that LLMs often struggle with internal token structures. Tasks such as identifying subsequences or common substrings highlight the limitations of conventional tokenization approaches, which fail to account for the hierarchical and compositional structures within language. Interestingly, the detailed analysis suggests that scaling model parameters might not fully mitigate these issues, as intrinsic token composition challenges persist.
3. Typographical Variation: The research further evaluates models' susceptibility to typographical perturbations at both character and token levels. The results indicate a marked degradation in performance when encountering typographical noise, highlighting an area where LLMs need significant improvement. This paper aligns with existing literature emphasizing the flaws in handling text complexity when token perturbations are present.

Mitigation and Forward-Looking Considerations

The introduction of BPE-dropout emerges as a promising method to enhance the resilience of LLMs to tokenization-related issues. By randomizing the tokenization process, BPE-dropout increases model robustness, suggesting a pathway for reducing tokenization's detrimental effects. However, the efficacy of this method varies across tasks, and scaling the dropout introduces unique challenges, such as exacerbating performance degradation with higher dropout rates.

The authors contribute valuable code and benchmarks, fostering further exploration in the field, encouraging researchers to build upon these findings, and refine tokenization techniques. Future advancements may explore tokenization-free approaches, drawing from recent methodologies that bypass traditional token limits in multilingual contexts. Moreover, addressing typographical variation holistically, including syntactic and semantic levels, may pave the way towards more resilient LLMs.

Conclusion

In summary, the paper provides an in-depth analysis of the shortcomings of traditional tokenization in the context of LLMs, underscoring areas for improvement and innovation. While parameter scaling offers partial mitigation, the inherent issues of token composition and sensitivity to typographical errors remain critical hurdles. As LLMs evolve, addressing these challenges will be crucial to enhancing their robustness and understanding their full potential across diverse linguistic landscapes.