AfroBench: How Good are Large Language Models on African Languages?

• URL: http://arxiv.org/abs/2311.07978v3
• Date: Wed, 26 Feb 2025 15:16:47 GMT
• Title: AfroBench: How Good are Large Language Models on African Languages?
• Authors: Jessica Ojo, Odunayo Ogundepo, Akintunde Oladipo, Kelechi Ogueji, Jimmy Lin, Pontus Stenetorp, David Ifeoluwa Adelani,
• Abstract summary: AfroBench is a benchmark for evaluating the performance of LLMs across 64 African languages.<n>AfroBench consists of nine natural language understanding datasets, six text generation datasets, six knowledge and question answering tasks, and one mathematical reasoning task.
• Score: 55.35674466745322
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale multilingual evaluations, such as MEGA, often include only a handful of African languages due to the scarcity of high-quality evaluation data and the limited discoverability of existing African datasets. This lack of representation hinders comprehensive LLM evaluation across a diverse range of languages and tasks. To address these challenges, we introduce AfroBench -- a multi-task benchmark for evaluating the performance of LLMs across 64 African languages, 15 tasks and 22 datasets. AfroBench consists of nine natural language understanding datasets, six text generation datasets, six knowledge and question answering tasks, and one mathematical reasoning task. We present results comparing the performance of prompting LLMs to fine-tuned baselines based on BERT and T5-style models. Our results suggest large gaps in performance between high-resource languages, such as English, and African languages across most tasks; but performance also varies based on the availability of monolingual data resources. Our findings confirm that performance on African languages continues to remain a hurdle for current LLMs, underscoring the need for additional efforts to close this gap. https://mcgill-nlp.github.io/AfroBench/

Related papers

• Lugha-Llama: Adapting Large Language Models for African Languages [48.97516583523523]
  Large language models (LLMs) have achieved impressive results in a wide range of natural language applications. We consider how to adapt LLMs to low-resource African languages. We find that combining curated data from African languages with high-quality English educational texts results in a training mix that substantially improves the model's performance on these languages.
  arXiv  Detail & Related papers  (2025-04-09T02:25:53Z)
• Bridging the Gap: Enhancing LLM Performance for Low-Resource African Languages with New Benchmarks, Fine-Tuning, and Cultural Adjustments [0.9214083577876088]
  This paper creates approximately 1 million human-translated words of new benchmark data in 8 low-resource African languages. Our benchmarks are translations of Winogrande and three sections of MMLU: college medicine, clinical knowledge, and virology. Using the benchmarks translated, we report previously unknown performance gaps between state-of-the-art (SOTA) LLMs in English and African languages.
  arXiv  Detail & Related papers  (2024-12-16T23:50:21Z)
• One Language, Many Gaps: Evaluating Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks [55.35278531907263]
  We present the first study on Large Language Models' fairness and robustness to a dialect in canonical reasoning tasks. We hire AAVE speakers to rewrite seven popular benchmarks, such as HumanEval and GSM8K. We find that, compared to Standardized English, almost all of these widely used models show significant brittleness and unfairness to queries in AAVE.
  arXiv  Detail & Related papers  (2024-10-14T18:44:23Z)
• Do Large Language Models Speak All Languages Equally? A Comparative Study in Low-Resource Settings [12.507989493130175]
  Large language models (LLMs) have garnered significant interest in natural language processing (NLP) Recent studies have highlighted the limitations of LLMs in low-resource languages. We present datasets for sentiment and hate speech tasks by translating from English to Bangla, Hindi, and Urdu.
  arXiv  Detail & Related papers  (2024-08-05T05:09:23Z)
• IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models [18.260317326787035]
  This paper introduces IrokoBench -- a human-translated benchmark dataset for 16 typologically-diverse low-resource African languages. We use IrokoBench to evaluate zero-shot, few-shot, and translate-test settings(where test sets are translated into English) across 10 open and four proprietary language models. We observe a significant performance gap between open and proprietary models, with the highest performing open model, Aya-101 only at 58% of the best-performing proprietary model GPT-4o performance.
  arXiv  Detail & Related papers  (2024-06-05T15:23:08Z)
• Zero-Shot Cross-Lingual Reranking with Large Language Models for Low-Resource Languages [51.301942056881146]
  We investigate how large language models (LLMs) function as rerankers in cross-lingual information retrieval systems for African languages. Our implementation covers English and four African languages (Hausa, Somali, Swahili, and Yoruba) We examine cross-lingual reranking with queries in English and passages in the African languages.
  arXiv  Detail & Related papers  (2023-12-26T18:38:54Z)
• Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations [59.056367787688146]
  This paper pioneers exploring and training powerful Multilingual Math Reasoning (xMR) LLMs. We construct the first multilingual math reasoning instruction dataset, MGSM8KInstruct, encompassing ten distinct languages. By utilizing translation, we construct the first multilingual math reasoning instruction dataset, MGSM8KInstruct, encompassing ten distinct languages.
  arXiv  Detail & Related papers  (2023-10-31T08:09:20Z)
• The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants [80.4837840962273]
  We present Belebele, a dataset spanning 122 language variants. This dataset enables the evaluation of text models in high-, medium-, and low-resource languages.
  arXiv  Detail & Related papers  (2023-08-31T17:43:08Z)
• ChatGPT for Arabic Grammatical Error Correction [5.945320097465418]
  Large language models (LLMs) fine-tuned to follow human instruction have exhibited significant capabilities in English NLP tasks. In this paper, we delve into abilities of instruction fine-tuned LLMs in Arabic GEC, a task made complex due to Arabic's rich morphology. We find that instruction fine-tuned models, regardless of their size, significantly underperform compared to fully fine-tuned models of significantly smaller sizes.
  arXiv  Detail & Related papers  (2023-08-08T18:00:39Z)
• Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts [75.33019401706188]
  Large language models (LLMs) are known to effectively perform tasks by simply observing few exemplars. We propose to assemble synthetic exemplars from a diverse set of high-resource languages to prompt the LLMs to translate from any language into English. Our unsupervised prompting method performs on par with supervised few-shot learning in LLMs of different sizes for translations between English and 13 Indic and 21 African low-resource languages.
  arXiv  Detail & Related papers  (2023-06-20T08:27:47Z)
• How Good are Commercial Large Language Models on African Languages? [0.012691047660244334]
  We present a preliminary analysis of commercial large language models on two tasks (machine translation and text classification) across eight African languages. Our results suggest that commercial language models produce below-par performance on African languages. In general, our findings present a call-to-action to ensure African languages are well represented in commercial large language models.
  arXiv  Detail & Related papers  (2023-05-11T02:29:53Z)
• AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages [0.021987601456703476]
  We present AfroLM, a multilingual language model pretrained from scratch on 23 African languages. AfroLM is pretrained on a dataset 14x smaller than existing baselines. It is able to generalize well across various domains.
  arXiv  Detail & Related papers  (2022-11-07T02:15:25Z)
• Crosslingual Generalization through Multitask Finetuning [80.8822603322471]
  Multitask prompted finetuning (MTF) has been shown to help large language models generalize to new tasks in a zero-shot setting. We apply MTF to the pretrained multilingual BLOOM and mT5 model families to produce finetuned variants called BLOOMZ and mT0. We find finetuning large multilingual language models on English tasks with English prompts allows for task generalization to non-English languages.
  arXiv  Detail & Related papers  (2022-11-03T13:19:32Z)
• MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition [55.95128479289923]
  African languages are spoken by over a billion people, but are underrepresented in NLP research and development. We create the largest human-annotated NER dataset for 20 African languages. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points.
  arXiv  Detail & Related papers  (2022-10-22T08:53:14Z)

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.