Nsanku: Evaluating Zero-Shot Translation Performance of LLMs for Ghanaian Languages

• URL: http://arxiv.org/abs/2605.04208v1
• Date: Tue, 05 May 2026 18:47:46 GMT
• Title: Nsanku: Evaluating Zero-Shot Translation Performance of LLMs for Ghanaian Languages
• Abstract summary: Large language models (LLMs) have demonstrated impressive multilingual capabilities for well-resourced languages.<n>Nsanku is a systematic benchmark that evaluates the zero-shot machine translation performance of 19 open-weight and proprietary LLMs across 43 Ghanaian languages.<n>No model and no language reached the Leaders quadrant of high performance and high consistency simultaneously.
• Score: 0.03138897474419886
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have demonstrated impressive multilingual capabilities for well-resourced languages, yet their performance on low-resource African languages remains poorly understood and largely unevaluated. This paper presents Nsanku, a systematic benchmark that evaluates the zero-shot machine translation performance of 19 open-weight and proprietary LLMs across 43 Ghanaian languages paired with English. Evaluation sentences were sourced from the YouVersion Bible platform, providing 300 sentence pairs per language. Two complementary automatic metrics are employed: Bilingual Evaluation Understudy (BLEU) and Character n-gram F-Score (chrF), alongside an average accuracy score and a cross-language consistency dimension. Nsanku represents the most comprehensive LLM translation evaluation for Ghanaian languages conducted to date. Results show that gemini-2.5-flash achieves the highest overall average score of 26.88 (BLEU: 24.60, chrF: 29.16), followed by claude-sonnet-4-5 at 24.87 (BLEU: 22.46, chrF: 27.28) and gpt-4.1 at 23.20 (BLEU: 21.15, chrF: 25.24). Among open-weight models, kimi-k2-instruct-0905 leads at an average score of 20.87. A critical finding from the consistency analysis is that no model and no language reached the Leaders quadrant of high performance and high consistency simultaneously, indicating that current LLMs are not yet reliably usable for Ghanaian language translation at scale. Siwu achieved the highest per-language average score at 25.73 while Nkonya scored lowest at 11.65. Nsanku establishes a publicly available, community-extensible evaluation infrastructure for African language NLP research.

Related papers

• Developing an English-Efik Corpus and Machine Translation System for Digitization Inclusion [0.0]
  This study evaluates the effectiveness of state-of-the-art multilingual neural machine translation models for English-Efik translation.<n>We fine-tuned both the mT5 multilingual model and the NLLB200 model on this dataset.<n>Our findings demonstrate the feasibility of developing practical machine translation tools for low-resource languages.
  arXiv  Detail & Related papers  (2026-03-16T06:17:22Z)
• PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts [85.78821098963607]
  PolyMath is a multilingual mathematical reasoning benchmark covering 18 languages and 4 easy-to-hard difficulty levels.<n>Our benchmark ensures difficulty comprehensiveness, language diversity, and high-quality translation.
  arXiv  Detail & Related papers  (2025-04-25T15:39:04Z)
• INJONGO: A Multicultural Intent Detection and Slot-filling Dataset for 16 African Languages [15.983678567785004]
  Slot-filling and intent detection are well-established tasks in Conversational AI.<n>We introduce Injongo -- a multicultural, open-source benchmark dataset for 16 African languages.<n>We show the advantage of leveraging African-cultural utterances over Western-centric utterances for improving cross-lingual transfer.
  arXiv  Detail & Related papers  (2025-02-13T23:17:10Z)
• NLIP_Lab-IITH Multilingual MT System for WAT24 MT Shared Task [9.476463361600826]
  This paper describes NLIP Lab's multilingual machine translation system for the WAT24 shared task on multilingual Indic MT task. We explore pre-training for Indic languages using alignment agreement objectives. We fine-tuned language direction-specific multilingual translation models using small and high-quality seed data.
  arXiv  Detail & Related papers  (2024-10-17T11:18:23Z)
• IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models [18.083861654053585]
  This paper introduces IrokoBench -- a human-translated benchmark dataset for 17 typologically-diverse low-resource African languages.<n>We use IrokoBench to evaluate zero-shot, few-shot, and translate-test settings(where test sets are translated into English) across 10 open and six proprietary language models.<n>We observe a significant performance gap between open and proprietary models, with the highest performing open model, Gemma 2 27B only at 63% of the best-performing proprietary model GPT-4o performance.
  arXiv  Detail & Related papers  (2024-06-05T15:23:08Z)
• CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models [53.9835961434552]
  We introduce the Chinese Instruction-Following Benchmark (CIF-Bench) to evaluate the generalizability of large language models (LLMs) to the Chinese language. CIF-Bench comprises 150 tasks and 15,000 input-output pairs, developed by native speakers to test complex reasoning and Chinese cultural nuances. To mitigate data contamination, we release only half of the dataset publicly, with the remainder kept private, and introduce diversified instructions to minimize score variance.
  arXiv  Detail & Related papers  (2024-02-20T16:02:12Z)
• 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)
• Assessing Translation capabilities of Large Language Models involving English and Indian Languages [4.067706269490143]
  We explore the multilingual capabilities of large language models by using machine translation as a task involving English and 22 Indian languages. We fine-tune these large language models using parameter efficient fine-tuning methods such as LoRA and additionally with full fine-tuning. Our results demonstrate significant progress, with average BLEU scores of 13.42, 15.93, 12.13, 12.30, and 12.07, as well as CHRF scores of 43.98, 46.99, 42.55, 42.42, and 45.39, respectively.
  arXiv  Detail & Related papers  (2023-11-15T18:58:19Z)
• ChatGPT MT: Competitive for High- (but not Low-) Resource Languages [62.178282377729566]
  Large language models (LLMs) implicitly learn to perform a range of language tasks, including machine translation (MT) We present the first experimental evidence for an expansive set of 204 languages, along with MT cost analysis. Our analysis reveals that a language's resource level is the most important feature in determining ChatGPT's relative ability to translate it.
  arXiv  Detail & Related papers  (2023-09-14T04:36:00Z)
• MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages [54.002969723086075]
  We evaluate cross-lingual open-retrieval question answering systems in 16 typologically diverse languages. The best system leveraging iteratively mined diverse negative examples achieves 32.2 F1, outperforming our baseline by 4.5 points. The second best system uses entity-aware contextualized representations for document retrieval, and achieves significant improvements in Tamil (20.8 F1), whereas most of the other systems yield nearly zero scores.
  arXiv  Detail & Related papers  (2022-07-02T06:54:10Z)
• AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages [75.08199398141744]
  We present AmericasNLI, an extension of XNLI (Conneau et al.), to 10 indigenous languages of the Americas. We conduct experiments with XLM-R, testing multiple zero-shot and translation-based approaches. We find that XLM-R's zero-shot performance is poor for all 10 languages, with an average performance of 38.62%.
  arXiv  Detail & Related papers  (2021-04-18T05:32:28Z)

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.