The Fair Language Model Paradox (2410.11985v1)

Abstract: LLMs are widely deployed in real-world applications, yet little is known about their training dynamics at the token level. Evaluation typically relies on aggregated training loss, measured at the batch level, which overlooks subtle per-token biases arising from (i) varying token-level dynamics and (ii) structural biases introduced by hyperparameters. While weight decay is commonly used to stabilize training, we reveal that it silently introduces performance biases detectable only at the token level. In fact, we empirically show across different dataset sizes, model architectures and sizes ranging from 270M to 3B parameters that as weight decay increases, low-frequency tokens are disproportionately depreciated. This is particularly concerning, as these neglected low-frequency tokens represent the vast majority of the token distribution in most languages, calling for novel regularization techniques that ensure fairness across all available tokens.

• The paper demonstrates that weight decay disproportionately degrades the performance of low-frequency tokens.
• It uses experiments on models ranging from 270M to 3B parameters to show higher cross-entropy loss and slower learning for infrequent tokens.
• The findings call for innovative, fairness-aware regularization methods to ensure equitable token treatment in language models.

An Expert Overview of "The Fair LLM Paradox"

Andrea Pinto, Tomer Galanti, and Randall Balestriero investigate the implications of weight decay in LLMs on token-level prediction accuracy, a critical yet underexplored area in LLM optimization. Their paper, "The Fair LLM Paradox," analyzes how weight decay—commonly employed for stabilization in model training—adversely affects low-frequency tokens, presenting new challenges for fairness in language modeling.

Key Findings

The authors empirically demonstrate that increased weight decay disproportionately impacts low-frequency tokens, which constitute the majority in typical language datasets. This bias occurs silently, as traditional metrics like average training loss do not account for token frequency nuances. Through experiments conducted on an IMDB dataset using models ranging from 270 million to 3 billion parameters, the authors illustrate that higher weight decay results in greater performance loss on these infrequently occurring tokens.

Significantly, they establish that low-frequency tokens experience higher cross-entropy loss and reduced learning speed compared to their high-frequency counterparts. This occurs because high-frequency tokens are learned consistently faster, benefiting more from the regularization.

Implications

The results challenge the traditional wisdom of using aggressive regularization to improve model generalization. By revealing a hidden pitfall—silent degradation of low-frequency token performance—this work prompts a re-evaluation of regularization techniques in NLP.

Practically, this suggests the need for novel regularization methods that ensure equitable treatment across token frequencies. Such innovations could mitigate token-level biases, fostering more inclusive LLMs capable of representing diverse linguistic patterns.

Theoretical Insights

The paper draws on theories related to weight decay and class imbalance from computer vision. By adapting these frameworks to LLMs, it reveals underlying reasons why low-frequency tokens are disadvantaged. Theoretical analyses, including those from related works, suggest that token frequency positively correlates with classifier weight norms, meaning low-frequency tokens naturally incur higher losses.

Future Directions

Future research should prioritize developing fairness-aware training regimes. Addressing token imbalance directly or modifying weight decay strategies to account for frequency could enhance model reliability without compromising token diversity. Additionally, extending analysis to other tokenization methods or larger vocabularies can offer insights into scaling this fairness approach.

Overall, the research highlights a neglected nuance in LLM training, advocating for a paradigm shift that integrates token-level equity into the core of LLM development frameworks.