Multilingual Performance Biases of Large Language Models in Education

• URL: http://arxiv.org/abs/2504.17720v1
• Date: Thu, 24 Apr 2025 16:32:31 GMT
• Title: Multilingual Performance Biases of Large Language Models in Education
• Authors: Vansh Gupta, Sankalan Pal Chowdhury, Vilém Zouhar, Donya Rooein, Mrinmaya Sachan,
• Abstract summary: Large language models (LLMs) are increasingly being adopted in educational settings.<n>This work ascertains if their use in education settings in non-English languages is warranted.
• Score: 39.14806026620442
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are increasingly being adopted in educational settings. These applications expand beyond English, though current LLMs remain primarily English-centric. In this work, we ascertain if their use in education settings in non-English languages is warranted. We evaluated the performance of popular LLMs on four educational tasks: identifying student misconceptions, providing targeted feedback, interactive tutoring, and grading translations in six languages (Hindi, Arabic, Farsi, Telugu, Ukrainian, Czech) in addition to English. We find that the performance on these tasks somewhat corresponds to the amount of language represented in training data, with lower-resource languages having poorer task performance. Although the models perform reasonably well in most languages, the frequent performance drop from English is significant. Thus, we recommend that practitioners first verify that the LLM works well in the target language for their educational task before deployment.

Related papers

• Improving Bilingual Capabilities of Language Models to Support Diverse Linguistic Practices in Education [3.799331337558008]
  Large language models (LLMs) offer promise in generating educational content, providing instructor feedback, and reducing teacher workload on assessments. We study the effectiveness of multilingual large language models (MLLMs) across monolingual (English-only, Spanish-only) and bilingual (Spanglish) student writing.
  arXiv  Detail & Related papers  (2024-11-06T23:16:25Z)
• Dictionary Insertion Prompting for Multilingual Reasoning on Multilingual Large Language Models [52.00446751692225]
  We present a novel and simple yet effective method called textbfDictionary textbfInsertion textbfPrompting (textbfDIP) When providing a non-English prompt, DIP looks up a word dictionary and inserts words' English counterparts into the prompt for LLMs. It then enables better translation into English and better English model thinking steps which leads to obviously better results.
  arXiv  Detail & Related papers  (2024-11-02T05:10:50Z)
• Better to Ask in English: Evaluation of Large Language Models on English, Low-resource and Cross-Lingual Settings [12.507989493130175]
  GPT-4, Llama 2, and Gemini are evaluated for their effectiveness in English compared to other low-resource languages from South Asia. Findings suggest GPT-4 outperformed Llama 2 and Gemini in all five prompt settings and across all languages.
  arXiv  Detail & Related papers  (2024-10-17T02:12:30Z)
• Pruning Multilingual Large Language Models for Multilingual Inference [28.36717615166238]
  This study explores how to enhance the zero-shot performance of MLLMs in non-English languages. We first analyze the behavior of MLLMs when performing translation and reveal that there are large magnitude features that play a critical role in the translation process.
  arXiv  Detail & Related papers  (2024-09-25T13:15:50Z)
• 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)
• Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners [67.85635044939836]
  Large Language Models (LLMs) have shown impressive language capabilities. In this work, we investigate the spontaneous multilingual alignment improvement of LLMs. We find that LLMs instruction-tuned on the question translation data (i.e. without annotated answers) are able to encourage the alignment between English and a wide range of languages.
  arXiv  Detail & Related papers  (2024-05-22T16:46:19Z)
• Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models [79.46179534911019]
  Large language models (LLMs) have demonstrated multilingual capabilities, yet they are mostly English-centric due to imbalanced training corpora.<n>We extend the evaluation to real-world user queries and non-English-centric LLMs, offering a broader examination of multilingual performance.
  arXiv  Detail & Related papers  (2024-03-15T12:47:39Z)
• 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)
• 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)

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.