A recent paper on arXiv cs.CL, "Apples to Apples? Towards Comparable Crosslingual Language Model Evaluation," addresses the challenge of fairly comparing language models across different languages. The authors note that existing studies employ diverse downstream tasks and intrinsic metrics, but the empirical validity of these approaches for drawing crosslingual conclusions has been underexplored. The research systematically investigates crosslingual evaluation methods.
Key Points
- Crosslingual evaluation of language models presents a fundamental challenge in multilingual natural language processing.
- Existing studies utilize various downstream tasks and intrinsic metrics with differing theoretical justifications.
- The paper empirically investigates whether current approaches yield meaningful crosslingual conclusions.
- Controlled monolingual language models, trained on parallel data with varying tokenizer vocabulary sizes and model sizes, were used for examination.
- Findings were further validated on multilingual large language models.
- Widely used normalized metrics introduce crosslinguistic biases stemming from tokenization, encoding, and orthographic differences.
- Sentence-level negative log-likelihood, computed over semantically equivalent sequences, offers more meaningful and consistent crosslingual comparisons.
Context
According to the authors, the study systematically examines crosslingual evaluation approaches. This involved using controlled monolingual language models that were trained on parallel data. These models varied in their tokenizer vocabulary sizes and overall model sizes. The findings derived from this controlled environment were then validated using multilingual large language models. The paper also discusses the inherent challenges in achieving comparable downstream evaluation across different languages.
Why It Matters
This research highlights a critical methodological issue for builders and researchers working with multilingual models: the potential for biased evaluations due to underlying linguistic differences. Understanding these biases is essential for accurately assessing model performance and making informed decisions about model development and deployment in diverse linguistic contexts.
What To Do
- Note that widely used normalized metrics may introduce crosslinguistic biases.
- Consider evaluating models using sentence-level negative log-likelihood over semantically equivalent sequences for more consistent crosslingual comparisons.
- Review the paper's discussion on challenges in achieving comparable downstream evaluation across languages.
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