A big challenge in evaluating the text comprehension abilities of multilingual models is the shortage of high-quality, simultaneous evaluation standards. There are high-coverage natural language processing datasets like FLORES-200, although they’re mostly used for machine translation. Although 100+ languages use understanding and generative text services, the shortage of labeled data presents a big barrier to constructing effective systems in most languages.

Significant scientific research is required beyond LLMs to enable the efficient and successful development of NLP systems for low-resource languages. While many modeling approaches claim to be language-independent, their applicability to a big selection of phenomena types is usually only tested in a small subset of languages.

A brand new study by Meta AI, Abridge AI, and Reka AI releases BELEBELE, a key benchmark for evaluating natural language understanding systems across 122 different language varieties. Each 488 paragraphs within the dataset has corresponding multiple-choice questions within the dataset’s 900 total questions. Questions distinguish between models with various levels of language comprehension competence and have been created with care. The questions are designed to reward generalizable NLU models and purposely penalize biased models, although they don’t require higher knowledge or reasoning. Questions asked in English may be answered with nearly perfect precision by humans. The various model outputs indicate that it is a discriminative NLU challenge, just like well-known LLM benchmarks like MMLU.

The BELEBELE system is the primary of its kind and is parallel across all languages. This enables for the primary direct comparison of model performance across languages. The info set includes 29 writing systems and 27 language families, representing various resource availability and linguistic diversity. Considered one of the primary natural language processing (NLP) benchmarks for the Romanized version of Hindi, Urdu, Bengali, Nepali, and Sinhala is predicated on these seven languages written in two different scripts.

The dataset’s parallel nature allows for the evaluation of cross-lingual textual representations in various cross-lingual scenarios, and it might be used to evaluate each monolingual and multilingual models. The duty could also be evaluated using full fine-tuning by piecing together a training set from comparable QA datasets. The researchers use quite a few masked language models (MLMs) for fine-tuning translations between languages and between English and other languages. Five-shot in-context learning and zero-shot (in-language and translate-test) evaluations are used to match different models for LLMs.

The findings show that while English-centric LLMs can go far and generalize to over 30 languages, models trained on medium- and low-resource languages profit most from a big vocabulary size and balanced pre-training data.

The team hopes their study helps improve existing model architectures and training methods by shedding light on how they handle multilingual data.

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