Are All Languages Equally Hard to Language-Model?

Abstract: For general modeling methods applied to diverse languages, a natural question is: how well should we expect our models to work on languages with differing typological profiles? In this work, we develop an evaluation framework for fair cross-linguistic comparison of LLMs, using translated text so that all models are asked to predict approximately the same information. We then conduct a study on 21 languages, demonstrating that in some languages, the textual expression of the information is harder to predict with both $n$-gram and LSTM LLMs. We show complex inflectional morphology to be a cause of performance differences among languages.

• The paper demonstrates that inflectional morphology is a major factor reducing language model performance, as shown by experiments on 21 languages.
• It employs a novel methodology using multi-text translations and a BPEC metric to equitably compare models across different languages.
• The study implies that current language models need architectural improvements to better handle the complexities of morphologically rich languages.

An Analysis of Cross-Linguistic Performance in Language Modeling

The paper "Are All Languages Equally Hard to Language-Model?" by Ryan Cotterell et al., investigates the extent to which linguistic typological differences affect the performance of LLMs. It posits that while most natural language processing methods are, in principle, applicable across languages, performance disparities exist when these models are applied to languages with complex inflectional morphologies. The paper develops a methodology for making cross-linguistic comparisons more equitable by using translated texts to ensure that models are tasked with predicting equivalent information across languages.

Methodological Approach

The researchers conducted experiments on 21 languages, utilizing both $n$-gram and LSTM-based LLMs. One of the novel facets of their methodology was the use of multi-texts, which are $k$-way translations of the same semantic content. This approach standardizes the evaluative condition by requiring models to predict the same underlying information across different languages. To account for orthographic variations, they introduced the metric of bits per English character (BPEC) rather than the conventional bits per character (BPC), thereby circumventing biases introduced by language-specific orthographic systems.

Key Findings

The study revealed that languages with rich inflectional morphology, such as Finnish and Hungarian, pose more significant challenges to LLMs compared to less inflected languages like English. Results demonstrated that inflectional morphology is a primary factor contributing to performance discrepancies in LLMs. Interestingly, when languages were lemmatized—transforming words into their base form by removing inflection—the correlation between morphological complexity and model performance disappeared. This finding indicates that the inflectional system of a language substantially contributes to its language modeling complexity.

Comparative Model Performance

$N$-gram models generally underperformed compared to LSTMs across all languages. However, both model types showed a noticeable decline in performance on highly inflected languages. The paper suggests that current model architectures may not effectively capture the syntactic and semantic complexities presented by rich morphological systems, due to limitations in modeling intermediate morphological elements.

Implications and Future Directions

The implications of this research are notable for the development of more language-agnostic NLP systems. The results suggest that existing LLMs need optimization or revision when applied to morphologically rich languages. Future research should explore architectural advancements or novel modeling approaches that can better accommodate inflectional morphology. Moreover, there is a need to discern whether the perceived difficulty arises from linguistic complexity inherent to certain languages or from intrinsic deficiencies in model design.

Conclusion

This paper provides rigorous analysis and empirical evidence concerning the variability in LLM performance attributable to linguistic typological features. By leveraging a cross-linguistic evaluation framework, it underscores the influence of inflectional morphology on the effectiveness of prevalent modeling techniques. The study sets a precedent for further inquiry into the adaptability of language processing models and highlights the necessity for innovations that account for linguistic diversity.