Apples to Apples? Towards Comparable Crosslingual Language Model Evaluation
Crosslingual evaluation of language models remains a fundamental challenge due to lack of empirical investigation into whether existing approaches yield meaningful comparisons The authors systematically examine evaluation approaches using controlled monolingual models trained on parallel data with varying tokenizer vocabulary sizes and model sizes, validated on multilingual LLMs Widely used normalized metrics introduce crosslinguistic biases rooted in tokenization, encoding, and orthographic dif
Analysis
TL;DR
- Crosslingual evaluation of language models remains a fundamental challenge due to lack of empirical investigation into whether existing approaches yield meaningful comparisons
- The authors systematically examine evaluation approaches using controlled monolingual models trained on parallel data with varying tokenizer vocabulary sizes and model sizes, validated on multilingual LLMs
- Widely used normalized metrics introduce crosslinguistic biases rooted in tokenization, encoding, and orthographic differences
- Sentence-level negative log-likelihood computed over semantically equivalent sequences provides more meaningful and consistent crosslingual comparisons
- The paper highlights significant challenges in achieving comparable downstream evaluation across languages
Why It Matters
This research directly addresses a critical gap in multilingual NLP: the lack of fair, comparable evaluation frameworks across languages. For AI practitioners building or benchmarking multilingual models, this work challenges commonly accepted evaluation practices and provides empirical evidence that many standard metrics are systematically biased, which could lead to incorrect conclusions about model capabilities across languages.
Technical Details
- The study uses controlled monolingual language models trained on parallel data with systematically varied tokenizer vocabulary sizes and model sizes to isolate the effects of these factors on crosslingual evaluation
- The authors validate their findings on multilingual LLMs, bridging the gap between controlled experiments and real-world model evaluation
- The paper identifies three specific sources of crosslinguistic bias: tokenization differences, encoding variations, and orthographic differences across languages
- The proposed alternative metric—sentence-level negative log-likelihood computed over semantically equivalent sequences—aims to normalize for these biases and enable fairer cross-lingual comparisons
- The work is situated in the computation and language (cs.CL) domain, addressing intrinsic metrics rather than downstream task performance
Industry Insight
- Researchers and practitioners should critically re-evaluate crosslingual benchmarking practices, as widely used normalized metrics may produce misleading rankings of model performance across languages
- When comparing multilingual models, sentence-level negative log-likelihood on semantically equivalent sequences should be considered as a more reliable intrinsic metric
- Tokenizer design and vocabulary size choices have downstream implications for crosslingual fairness that are often overlooked; evaluation pipelines should account for these structural biases when reporting multilingual capabilities
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