Summary of Bridging the Gap: Enhancing Llm Performance For Low-resource African Languages with New Benchmarks, Fine-tuning, and Cultural Adjustments, by Tuka Alhanai et al.
Bridging the Gap: Enhancing LLM Performance for Low-Resource African Languages with New Benchmarks, Fine-Tuning, and Cultural Adjustments
by Tuka Alhanai, Adam Kasumovic, Mohammad Ghassemi, Aven Zitzelberger, Jessica Lundin, Guillaume Chabot-Couture
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper aims to address the significant disparities in Large Language Models (LLMs) performance for non-English languages, particularly native African languages. To bridge this gap, the authors create a new benchmark dataset of approximately 1 million human-translated words across eight low-resource African languages, including Amharic, Bambara, Igbo, Sepedi, Shona, Sesotho, Setswana, and Tsonga. The benchmarks are translations of Winogrande and three sections of MMLU: college medicine, clinical knowledge, and virology. Using these translated benchmarks, the authors report previously unknown performance gaps between state-of-the-art LLMs in English and African languages. To reduce this gap, they explore methods such as high-quality dataset fine-tuning (using an LLM-as-an-Annotator), cross-lingual transfer, and cultural appropriateness adjustments. The study finds average mono-lingual improvements of 5.6% with fine-tuning, 2.9% average gains from cross-lingual transfer, and a 3.0% out-of-the-box performance boost on culturally appropriate questions. The publicly available benchmarks, translations, and code support further research and development aimed at creating more inclusive language technologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to fix a big problem in language models. Right now, these models are really good at understanding English, but they’re not very good at understanding other languages, especially African languages that don’t have many words or sentences written down. The authors of this paper create a new set of words and sentences in eight African languages to help them understand better. They then compare the performance of language models on these African languages to how well they do on English. To make things better, they also try out different ways to improve the models’ understanding, such as using more accurate training data or transferring what they’ve learned from one language to another. The results show that with a little extra help, language models can understand African languages much better. |
Keywords
» Artificial intelligence » Fine tuning
