The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants
- URL: http://arxiv.org/abs/2308.16884v2
- Date: Thu, 25 Jul 2024 04:30:15 GMT
- Title: The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants
- Authors: Lucas Bandarkar, Davis Liang, Benjamin Muller, Mikel Artetxe, Satya Narayan Shukla, Donald Husa, Naman Goyal, Abhinandan Krishnan, Luke Zettlemoyer, Madian Khabsa,
- Abstract summary: We present Belebele, a dataset spanning 122 language variants.
This dataset enables the evaluation of text models in high-, medium-, and low-resource languages.
- Score: 80.4837840962273
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: We present Belebele, a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. Significantly expanding the language coverage of natural language understanding (NLU) benchmarks, this dataset enables the evaluation of text models in high-, medium-, and low-resource languages. Each question is based on a short passage from the Flores-200 dataset and has four multiple-choice answers. The questions were carefully curated to discriminate between models with different levels of general language comprehension. The English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. We use this dataset to evaluate the capabilities of multilingual masked language models (MLMs) and large language models (LLMs). We present extensive results and find that despite significant cross-lingual transfer in English-centric LLMs, much smaller MLMs pretrained on balanced multilingual data still understand far more languages. We also observe that larger vocabulary size and conscious vocabulary construction correlate with better performance on low-resource languages. Overall, Belebele opens up new avenues for evaluating and analyzing the multilingual capabilities of NLP systems.
Related papers
- Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models [62.91524967852552]
Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora.
But can these models relate corresponding concepts across languages, effectively being crosslingual?
This study evaluates six state-of-the-art LLMs on inherently crosslingual tasks.
arXiv Detail & Related papers (2024-06-23T15:15:17Z) - Tagengo: A Multilingual Chat Dataset [3.8073142980733]
We present a high quality dataset of more than 70k prompt-response pairs in 74 languages.
We use this dataset to train a state-of-the-art open source English LLM to chat multilingually.
arXiv Detail & Related papers (2024-05-21T09:06:36Z) - SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic
Classification in 200+ Languages and Dialects [9.501383449039142]
We created SIB-200 -- a large-scale benchmark dataset for topic classification in 200 languages and dialects.
For many of the languages covered in SIB-200, this is the first publicly available evaluation dataset for Natural Language Understanding.
We found that languages unseen during the pre-training of multilingual language models, under-represented language families, and languages from the regions of Africa, Americas, Oceania and South East Asia often have the lowest performance on our topic classification dataset.
arXiv Detail & Related papers (2023-09-14T05:56:49Z) - Extrapolating Large Language Models to Non-English by Aligning Languages [109.09051737966178]
Existing large language models show disparate capability across different languages.
In this paper, we empower pre-trained LLMs on non-English languages by building semantic alignment across languages.
arXiv Detail & Related papers (2023-08-09T13:32:06Z) - Investigating the Translation Performance of a Large Multilingual
Language Model: the Case of BLOOM [8.858671209228536]
We focus on BLOOM's multilingual ability by evaluating its machine translation performance across several datasets.
We study several aspects including prompt design, model sizes, cross-lingual transfer and the use of discursive context.
arXiv Detail & Related papers (2023-03-03T13:23:42Z) - Cross-lingual Machine Reading Comprehension with Language Branch
Knowledge Distillation [105.41167108465085]
Cross-lingual Machine Reading (CLMRC) remains a challenging problem due to the lack of large-scale datasets in low-source languages.
We propose a novel augmentation approach named Language Branch Machine Reading (LBMRC)
LBMRC trains multiple machine reading comprehension (MRC) models proficient in individual language.
We devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
arXiv Detail & Related papers (2020-10-27T13:12:17Z) - XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning [68.57658225995966]
Cross-lingual Choice of Plausible Alternatives (XCOPA) is a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages.
We evaluate a range of state-of-the-art models on this novel dataset, revealing that the performance of current methods falls short compared to translation-based transfer.
arXiv Detail & Related papers (2020-05-01T12:22:33Z) - Learning to Scale Multilingual Representations for Vision-Language Tasks [51.27839182889422]
The effectiveness of SMALR is demonstrated with ten diverse languages, over twice the number supported in vision-language tasks to date.
We evaluate on multilingual image-sentence retrieval and outperform prior work by 3-4% with less than 1/5th the training parameters compared to other word embedding methods.
arXiv Detail & Related papers (2020-04-09T01:03:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.