MEXA: Multilingual Evaluation of English-Centric LLMs via Cross-Lingual
  Alignment
English-centric large language models (LLMs) often show strong multilingual
capabilities. However, the multilingual performance of these models remains
unclear and is not thoroughly evaluated for many languages. Most benchmarks for
multilinguality focus on classic NLP tasks, or cover a minimal number of
languages. We introduce MEXA, a method for assessing the multilingual
capabilities of pre-trained English-centric LLMs using parallel sentences,
which are available for more languages than existing downstream tasks. MEXA
leverages the fact that English-centric LLMs use English as a kind of pivot
language in their intermediate layers. It computes the alignment between
English and non-English languages using parallel sentences to evaluate the
transfer of language understanding from English to other languages. This
alignment can be used to estimate model performance in other languages. We
conduct studies using various parallel datasets (FLORES-200 and Bible), models
(Llama family, Gemma family, Mistral, and OLMo), and established downstream
tasks (Belebele, m-MMLU, and m-ARC). We explore different methods to compute
embeddings in decoder-only models. Our results show that MEXA, in its default
settings, achieves a statistically significant average Pearson correlation of
0.90 with three established downstream tasks across nine models and two
parallel datasets. This suggests that MEXA is a reliable method for estimating
the multilingual capabilities of English-centric LLMs, providing a clearer
understanding of their multilingual potential and the inner workings of LLMs.
Leaderboard: https://huggingface.co/spaces/cis-lmu/Mexa, Code:
https://github.com/cisnlp/Mexa.
6707e251500b1d1f3b493f2b
Amir Hossein Kargaran
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2024-10-25T09:31:22.705Z
61bf84c8ca59d6d196a1b4e8
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Amir Hossein Kargaran
user
kargaranamir
6707e251500b1d1f3b493f2c
Ali Modarressi
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2024-10-15T14:19:35.792Z
60e4738a8c0ddd18fc27ff88
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Ali Modarressi
user
amodaresi
6707e251500b1d1f3b493f2d
Nafiseh Nikeghbal
6707e251500b1d1f3b493f2e
Jana Diesner
6707e251500b1d1f3b493f2f
François Yvon
6707e251500b1d1f3b493f30
Hinrich Schütze
MEXA: Multilingual Evaluation of English-Centric LLMs via Cross-Lingual
  Alignment
English-centric large language models (LLMs) often show strong multilingual
capabilities. However, the multilingual performance of these models remains
unclear and is not thoroughly evaluated for many languages. Most benchmarks for
multilinguality focus on classic NLP tasks, or cover a minimal number of
languages. We introduce MEXA, a method for assessing the multilingual
capabilities of pre-trained English-centric LLMs using parallel sentences,
which are available for more languages than existing downstream tasks. MEXA
leverages the fact that English-centric LLMs use English as a kind of pivot
language in their intermediate layers. It computes the alignment between
English and non-English languages using parallel sentences to evaluate the
transfer of language understanding from English to other languages. This
alignment can be used to estimate model performance in other languages. We
conduct studies using various parallel datasets (FLORES-200 and Bible), models
(Llama family, Gemma family, Mistral, and OLMo), and established downstream
tasks (Belebele, m-MMLU, and m-ARC). We explore different methods to compute
embeddings in decoder-only models. Our results show that MEXA, in its default
settings, achieves a statistically significant average Pearson correlation of
0.90 with three established downstream tasks across nine models and two
parallel datasets. This suggests that MEXA is a reliable method for estimating
the multilingual capabilities of English-centric LLMs, providing a clearer
understanding of their multilingual potential and the inner workings of LLMs.
Leaderboard: https://huggingface.co/spaces/cis-lmu/Mexa, Code:
https://github.com/cisnlp/Mexa.
MEXA assesses the multilingual capabilities of English-centric LLMs using parallel sentences, providing a reliable method for estimating their performance across various languages and tasks.
pre-trained English-centric LLMs parallel sentences pivot language language understanding alignment embeddings decoder-only models Belebele m-MMLU m-ARC Pearson correlation