Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies

Abstract: Gender-inclusive NLP research has documented the harmful limitations of gender binary-centric LLMs (LLM), such as the inability to correctly use gender-diverse English neopronouns (e.g., xe, zir, fae). While data scarcity is a known culprit, the precise mechanisms through which scarcity affects this behavior remain underexplored. We discover LLM misgendering is significantly influenced by Byte-Pair Encoding (BPE) tokenization, the tokenizer powering many popular LLMs. Unlike binary pronouns, BPE overfragments neopronouns, a direct consequence of data scarcity during tokenizer training. This disparate tokenization mirrors tokenizer limitations observed in multilingual and low-resource NLP, unlocking new misgendering mitigation strategies. We propose two techniques: (1) pronoun tokenization parity, a method to enforce consistent tokenization across gendered pronouns, and (2) utilizing pre-existing LLM pronoun knowledge to improve neopronoun proficiency. Our proposed methods outperform finetuning with standard BPE, improving neopronoun accuracy from 14.1% to 58.4%. Our paper is the first to link LLM misgendering to tokenization and deficient neopronoun grammar, indicating that LLMs unable to correctly treat neopronouns as pronouns are more prone to misgender.

• The paper demonstrates that standard BPE tokenization overly fragments neopronouns, leading to syntactic challenges and misgendering in NLP models.
• It introduces metrics for assessing misgendering errors, linking token fragmentation directly to performance deficits in tasks like POS tagging.
• The study proposes Pronoun Tokenization Parity and lexical finetuning, achieving neopronoun consistency improvement from 14.1% to 58.4%.

Overview of "Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies"

The paper entitled "Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies" investigates the intersection of gender inclusivity and NLP, focusing on the disparate tokenization patterns for gendered and non-binary pronouns by LLMs. The authors, Anaelia Ovalle et al., undertake a detailed exploration of how Byte-Pair Encoding (BPE), the prevalent tokenization mechanism, disproportionately fragments neopronouns. This underrepresentation leads to syntactic difficulties and misgendering by models trained on corpora lacking sufficient gender-diverse data.

Key Contributions and Findings

1. Impact of BPE Tokenization:
   • The research highlights that BPE tokenization results in overfragmentation of neopronouns due to their low frequency in training datasets. Unlike binary pronouns, which are typically preserved as single tokens, neopronouns are split into multiple subword tokens.
   • This fragmentation complicates the processing of syntactic structures, leading to poor performance in tasks such as Part-of-Speech (POS) tagging and dependency parsing, as evidenced by substantial prior work.
2. Evaluation of Misgendering:
   • The paper develops a series of metrics to assess LLMs on their use of neopronouns. The metrics include pronoun consistency, case agreement errors, and adversarial injection errors.
   • They found that these errors correlate strongly with the extent of token fragmentation, pointing to a direct link between poor tokenization handling and misgendering.
3. Mitigation Strategies:
   • Two primary strategies are proposed to enhance LLMs' handling of neopronouns: Pronoun Tokenization Parity (PTP) and leveraging existing LLM pronoun knowledge.
   • PTP aligns tokenization of neopronouns with that of binary pronouns by introducing new token embeddings and aims to preserve the morphemic integrity of pronouns.
   • The modified finetuning approach, which involves only adjusting the lexical layer, leverages the pre-existing knowledge in LLMs to improve context-related performance without retraining the entire network.
4. Experimental Results:
   • Experiments using PTP and lexical layer finetuning show a significant increase in neopronoun consistency, from 14.1% with standard BPE finetuning to 58.4%.
   • These results were consistent across varying LLM model sizes and demonstrated a reduction in adversarial injection errors, thus underscoring the utility of enhancing grammatical proficiency to reduce misgendering.

Implications and Future Directions

The findings in this paper underscore the critical role of tokenization in developing more inclusive NLP systems. The proposed strategies not only improve the accuracy of LLMs in handling neopronouns but also offer a blueprint for future work in NLP focusing on fairness and inclusivity. By advancing methods to handle low-resource tokenization challenges, this work could extend its applications to multilingual NLP tasks and other linguistic scenarios where token frequency disparity leads to biased model behavior.

Future research deriving from these findings could involve exploring the scalability of PTP in multilingual LLMs and testing new tokenization algorithms that inherently balance representation across diverse linguistic categories. As NLP systems increasingly become integrated into societal structures, ensuring their inclusiveness and fairness remains a priority, and this paper provides a valuable contribution toward that endeavor in gender-inclusive language technologies.