Blessing of Multilinguality: A Systematic Analysis of Multilingual In-Context Learning

• URL: http://arxiv.org/abs/2502.11364v2
• Date: Tue, 18 Feb 2025 12:42:47 GMT
• Title: Blessing of Multilinguality: A Systematic Analysis of Multilingual In-Context Learning
• Authors: Yilei Tu, Andrew Xue, Freda Shi,
• Abstract summary: We show that demonstrations in mixed HRLs consistently outperform English-only ones.<n>Surprisingly, our ablation study shows that the presence of irrelevant non-English sentences in the prompt yields measurable gains.
• Score: 6.66199855001719
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While multilingual large language models generally perform adequately, and sometimes even rival English performance on high-resource languages (HRLs), they often significantly underperform on low-resource languages (LRLs). Among several prompting strategies aiming at bridging the gap, multilingual in-context learning (ICL) has been particularly effective when demonstration in target languages is unavailable. However, there lacks a systematic understanding of when and why it works well. In this work, we systematically analyze multilingual ICL, using demonstrations in HRLs to enhance cross-lingual transfer. We show that demonstrations in mixed HRLs consistently outperform English-only ones across the board, particularly for tasks written in LRLs. Surprisingly, our ablation study shows that the presence of irrelevant non-English sentences in the prompt yields measurable gains, suggesting the effectiveness of multilingual exposure itself. Our results highlight the potential of strategically leveraging multilingual resources to bridge the performance gap for underrepresented languages.

Related papers

• Towards Inclusive NLP: Assessing Compressed Multilingual Transformers across Diverse Language Benchmarks [33.2185998586144]
  This study benchmarks the performance of multilingual and monolingual Large Language Models (LLMs) across Arabic, English, and Indic languages.<n>Findings show significant performance differences driven by linguistic diversity and resource availability.<n> Quantization (4-bit and 8-bit) is effective in maintaining model accuracy while promoting efficiency, but aggressive pruning significantly compromises performance.
  arXiv  Detail & Related papers  (2025-07-25T22:35:10Z)
• How and Where to Translate? The Impact of Translation Strategies in Cross-lingual LLM Prompting [15.388822834013599]
  In multilingual retrieval-augmented generation (RAG)-based systems, knowledge bases (KB) are often shared from high-resource languages (such as English) to low-resource ones.<n>Two common practices are pre-translation to create a mono-lingual prompt and cross-lingual prompting for direct inference.<n>We show that an optimized prompting strategy can significantly improve knowledge sharing across languages, therefore improve the performance on the downstream classification task.
  arXiv  Detail & Related papers  (2025-07-21T19:37:15Z)
• The Emergence of Abstract Thought in Large Language Models Beyond Any Language [95.50197866832772]
  Large language models (LLMs) function effectively across a diverse range of languages.<n>Preliminary studies observe that the hidden activations of LLMs often resemble English, even when responding to non-English prompts.<n>Recent results show strong multilingual performance, even surpassing English performance on specific tasks in other languages.
  arXiv  Detail & Related papers  (2025-06-11T16:00:54Z)
• CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention [56.08815340137881]
  Large Vision-Language Models (LVLMs) have demonstrated impressive multimodal abilities but remain prone to multilingual object hallucination.<n>We propose Cross-Lingual Attention Intervention for Mitigating multilingual object hallucination (CLAIM) in LVLMs, a novel near training-free method by aligning attention patterns.
  arXiv  Detail & Related papers  (2025-06-03T11:17:16Z)
• Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models [55.14276067678253]
  This paper introduces a novel methodology for efficiently identifying inherent cross-lingual weaknesses in Large Language Models (LLMs)<n>We construct a new dataset of over 6,000 bilingual pairs across 16 languages using this methodology, demonstrating its effectiveness in revealing weaknesses even in state-of-the-art models.<n>Further experiments investigate the relationship between linguistic similarity and cross-lingual weaknesses, revealing that linguistically related languages share similar performance patterns.
  arXiv  Detail & Related papers  (2025-05-24T12:31:27Z)
• When Less Language is More: Language-Reasoning Disentanglement Makes LLMs Better Multilingual Reasoners [111.50503126693444]
  We show that language-specific ablation consistently boosts multilingual reasoning performance.<n>Compared to post-training, our training-free ablation achieves comparable or superior results with minimal computational overhead.
  arXiv  Detail & Related papers  (2025-05-21T08:35:05Z)
• Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task [73.35882908048423]
  Retrieval-augmented generation (RAG) has become a cornerstone of contemporary NLP. This paper investigates the effectiveness of RAG across multiple languages by proposing novel approaches for multilingual open-domain question-answering.
  arXiv  Detail & Related papers  (2025-04-04T17:35:43Z)
• On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation [7.478369203246005]
  Retrieval-augmented generation (RAG) with large language models (LLMs) has demonstrated strong performance in multilingual question-answering tasks. In multilingual RAG, retrieved passages can be written in languages other than that of the query entered by the user.
  arXiv  Detail & Related papers  (2025-04-01T09:55:23Z)
• Lens: Rethinking Multilingual Enhancement for Large Language Models [70.85065197789639]
  Lens is a novel approach to enhance multilingual capabilities of large language models (LLMs) It operates by manipulating the hidden representations within the language-agnostic and language-specific subspaces from top layers of LLMs. It achieves superior results with much fewer computational resources compared to existing post-training approaches.
  arXiv  Detail & Related papers  (2024-10-06T08:51:30Z)
• 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)
• 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)
• Teaching LLMs to Abstain across Languages via Multilingual Feedback [40.84205285309612]
  We show that multilingual feedback helps identify knowledge gaps across diverse languages, cultures, and communities. Extensive experiments demonstrate that our multilingual feedback approach outperforms various strong baselines. Further analysis reveals that multilingual feedback is both an effective and a more equitable abstain strategy to serve diverse language speakers.
  arXiv  Detail & Related papers  (2024-06-22T21:59:12Z)
• 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)
• LLMs Are Few-Shot In-Context Low-Resource Language Learners [59.74451570590808]
  In-context learning (ICL) empowers large language models (LLMs) to perform diverse tasks in underrepresented languages. We extensively study ICL and its cross-lingual variation (X-ICL) on 25 low-resource and 7 relatively higher-resource languages. Our study concludes the significance of few-shot in-context information on enhancing the low-resource understanding quality of LLMs.
  arXiv  Detail & Related papers  (2024-03-25T07:55:29Z)
• 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. 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)
• Mitigating the Linguistic Gap with Phonemic Representations for Robust Cross-lingual Transfer [26.014079273740485]
  Approaches to improving multilingual language understanding often struggle with significant performance gaps between high-resource and low-resource languages. We present experiments on three representative cross-lingual tasks on 12 languages in total. Phonemic representations exhibit higher similarities between languages compared to orthographic representations.
  arXiv  Detail & Related papers  (2024-02-22T04:41:52Z)
• 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)
• Don't Trust ChatGPT when Your Question is not in English: A Study of Multilingual Abilities and Types of LLMs [16.770697902481107]
  Large Language Models (LLMs) have demonstrated exceptional natural language understanding abilities. We propose a systematic way of qualifying the performance disparities of LLMs under multilingual settings. The results show that GPT exhibits highly translating-like behaviour in multilingual settings.
  arXiv  Detail & Related papers  (2023-05-24T02:05:03Z)
• Multilingual Large Language Models Are Not (Yet) Code-Switchers [41.47534626749588]
  Large Language Models (LLMs) have recently shown great capabilities in a wide range of tasks. The practice of alternating languages within an utterance remains relatively uncharted. We argue that current "multilingualism" in LLMs does not inherently imply proficiency with code-switching texts.
  arXiv  Detail & Related papers  (2023-05-23T16:50:48Z)

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.