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The Impact of Near Duplicate Subwords on Language Model Efficiency


Core Concepts
Near duplicate subwords in language model vocabularies can negatively impact training efficiency, but merging them may not yield the expected performance improvements.
Abstract
The paper investigates the impact of near duplicate subwords on the performance of language models (LMs). It first conducts experiments in a controlled setting where the vocabulary is synthetically duplicated, allowing the authors to quantify an upper bound on the potential gains from improved generalization across duplicates. They find that LMs trained on a fully duplicated vocabulary require around 17% more data to achieve the same performance as LMs trained on the original vocabulary. The authors then examine the impact of naturally occurring near duplicates in LM vocabularies. Contrary to the synthetic setting, they find that merging these near duplicates generally hurts model performance instead of improving it. This suggests that real near duplicates are not as equivalent as anticipated, and the information lost by merging them is more detrimental than the potential gains from improved generalization. The paper further analyzes the representations learned by the LMs, showing that frequent duplicate subwords tend to have highly aligned embeddings, which enables some degree of generalization. However, this alignment is less pronounced for infrequent subwords, and the presence of deduplicated tokens in the input context tends to hurt prediction performance. Finally, the authors experiment with re-injecting information about the original subwords through a shared learned embedding, which can mitigate some of the performance losses from deduplication, but does not achieve the same benefits as observed in the synthetic setting.
Stats
The vocabulary of modern large language models contains around 40% near duplicate subwords.
Quotes
"Intuitively, if the model had access to character-level information, it should trivially generalise what it learns from one of these forms to the other. Given only access to subword-level inputs, however, the model may not be able to do the same, or may require more data to do so." "Contrary to the synthetic setting, they find that merging these near duplicates generally hurts model performance instead of improving it. This suggests that real near duplicates are not as equivalent as anticipated, and the information lost by merging them is more detrimental than the potential gains from improved generalization."

Deeper Inquiries

How would the findings of this paper change if the experiments were conducted on a more diverse set of languages beyond English

Conducting the experiments on a more diverse set of languages beyond English would likely provide valuable insights into how near duplicate subwords impact language modeling performance across different linguistic structures and writing systems. The findings could potentially reveal variations in the semantic relationships between near duplicates in different languages, shedding light on the generalizability of language models across diverse linguistic contexts. Additionally, the impact of near duplicates on model efficiency and data efficiency may vary across languages with distinct morphological, syntactic, and orthographic characteristics. By exploring a broader range of languages, the study could uncover language-specific patterns in near duplicate subwords and their effects on language modeling tasks.

What other techniques, beyond the shared learned embedding approach, could be used to leverage the similarities between near duplicate subwords without losing important information

Beyond the shared learned embedding approach, several other techniques could be employed to leverage the similarities between near duplicate subwords without sacrificing important information. One approach could involve incorporating contextual information to differentiate between near duplicates during the training process. By considering the surrounding context of near duplicate subwords, the model could learn to distinguish subtle semantic differences and make more accurate predictions. Additionally, ensemble learning techniques could be utilized to combine the predictions of models trained on both the original and deduplicated data, leveraging the strengths of each model to improve overall performance. Furthermore, incorporating linguistic features or constraints specific to near duplicate pairs could help the model better capture their nuanced distinctions while maintaining efficiency.

Could the insights from this work be applied to improve the design of tokenization algorithms used in language models, to better balance the trade-offs between vocabulary size, information loss, and model efficiency

The insights from this work could be applied to enhance the design of tokenization algorithms used in language models, aiming to strike a balance between vocabulary size, information loss, and model efficiency. One potential application is the development of adaptive tokenization strategies that dynamically adjust the tokenization process based on the presence of near duplicate subwords. By identifying and treating near duplicates differently during tokenization, algorithms can minimize information loss while optimizing vocabulary size for improved model performance. Additionally, exploring subword fusion techniques that merge near duplicates in a more nuanced manner, considering their semantic similarities and differences, could lead to more effective tokenization methods. By integrating the findings from this study into tokenization algorithms, researchers can optimize language model training by preserving essential information while managing vocabulary complexity.
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