EMCee: Improving Multilingual Capability of LLMs via Bridging Knowledge and Reasoning with Extracted Synthetic Multilingual Context

• URL: http://arxiv.org/abs/2503.05846v2
• Date: Fri, 17 Oct 2025 07:19:54 GMT
• Title: EMCee: Improving Multilingual Capability of LLMs via Bridging Knowledge and Reasoning with Extracted Synthetic Multilingual Context
• Authors: Hamin Koo, Jaehyung Kim,
• Abstract summary: Large Language Models (LLMs) have achieved impressive progress across a wide range of tasks.<n> heavy reliance on English-centric training data leads to significant performance degradation in non-English languages.<n>We propose EMCee, a framework that enhances the multilingual capabilities of LLMs by explicitly extracting and utilizing query-relevant knowledge.
• Score: 6.612630497074871
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Language Models (LLMs) have achieved impressive progress across a wide range of tasks, yet their heavy reliance on English-centric training data leads to significant performance degradation in non-English languages. While existing multilingual prompting methods emphasize reformulating queries into English or enhancing reasoning capabilities, they often fail to incorporate the language- and culture-specific grounding that is essential for some queries. To address this limitation, we propose EMCee (Extracting synthetic Multilingual Context and merging), a simple yet effective framework that enhances the multilingual capabilities of LLMs by explicitly extracting and utilizing query-relevant knowledge from the LLM itself. In particular, EMCee first extracts synthetic context to uncover latent, language-specific knowledge encoded within the LLM, and then dynamically merges this contextual insight with reasoning-oriented outputs through a judgment-based selection mechanism. Extensive experiments on four multilingual benchmarks covering diverse languages and tasks demonstrate that EMCee consistently outperforms prior approaches, achieving an average relative improvement of 16.4% overall and 31.7% in low-resource languages.

Related papers

• Unlocking Multilingual Reasoning Capability of LLMs and LVLMs through Representation Engineering [44.596434537567916]
  We propose a training-free inference-time method to enhance Multilingual Reasoning capabilities via Representation Engineering (MRRE)<n>MRRE consistently enhances non-English reasoning by an average gain of 5.48% and up to 7.54% in low-resource languages (Thai and Swahili)
  arXiv  Detail & Related papers  (2025-11-28T14:40:27Z)
• Code-Switching In-Context Learning for Cross-Lingual Transfer of Large Language Models [64.54005959758733]
  We introduce code-switching in-context learning (CSICL) as a principled and robust approach for overcoming the translation barrier during inference.<n>We conduct extensive experiments across 4 LLMs, 6 datasets, and 10 languages, spanning both knowledge-intensive and reasoning-oriented domains.<n>Our results demonstrate CSICL consistently outperforms X-ICL baselines, achieving gains of 3.1%p and 1.9%p in both target and unseen languages.
  arXiv  Detail & Related papers  (2025-10-07T08:35:42Z)
• A method for improving multilingual quality and diversity of instruction fine-tuning datasets [29.07537849245622]
  We introduce Multilingual Data Quality and Diversity (M-DaQ) to improve Multilingual Instruction Fine-Tuning (IFT)<n>M-DaQ is a novel method for improving LLMs multilinguality by selecting high-quality and semantically diverse multilingual IFT samples.<n> Empirical results across 18 languages demonstrate that models fine-tuned with M-DaQ achieve significant performance gains over vanilla baselines over 60% win rate.
  arXiv  Detail & Related papers  (2025-09-19T03:07:59Z)
• On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation [12.848952248427977]
  Retrieval-augmented generation (RAG) with large language models (LLMs) has demonstrated strong performance in multilingual question-answering tasks.<n>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)
• MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation [86.7047714187813]
  MMLU-ProX is a benchmark covering 29 languages, built on an English benchmark.<n>Each language version consists of 11,829 identical questions, enabling direct cross-linguistic comparisons.<n>To meet efficient evaluation needs, we provide a lite version containing 658 questions per language.
  arXiv  Detail & Related papers  (2025-03-13T15:59:20Z)
• Balanced Multi-Factor In-Context Learning for Multilingual Large Language Models [53.38288894305388]
  Multilingual large language models (MLLMs) are able to leverage in-context learning (ICL) to achieve high performance by leveraging cross-lingual knowledge transfer without parameter updates.<n>Three key factors influence multilingual ICL: (1) semantic similarity, (2) linguistic alignment, and (3) language-specific performance.<n>We propose balanced multi-factor ICL (textbfBMF-ICL), a method that quantifies and optimally balances these factors for improved example selection.
  arXiv  Detail & Related papers  (2025-02-17T06:56:33Z)
• LUSIFER: Language Universal Space Integration for Enhanced Multilingual Embeddings with Large Language Models [89.13128402847943]
  We present LUSIFER, a novel zero-shot approach that adapts LLM-based embedding models for multilingual tasks without requiring multilingual supervision.<n>LUSIFER's architecture combines a multilingual encoder, serving as a language-universal learner, with an LLM-based embedding model optimized for embedding-specific tasks.<n>We introduce a new benchmark encompassing 5 primary embedding tasks, 123 diverse datasets, and coverage across 14 languages.
  arXiv  Detail & Related papers  (2025-01-01T15:43:07Z)
• Extracting and Combining Abilities For Building Multi-lingual Ability-enhanced Large Language Models [109.60937659029076]
  We propose a Multi-lingual Extraction and Combination approach, named as MAEC.<n>Our key idea is to decompose and extract language-agnostic ability-related weights from large language models.<n>Experiment results have shown that MAEC can effectively and efficiently extract and combine the advanced abilities.
  arXiv  Detail & Related papers  (2024-10-10T11:23:18Z)
• 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)
• LLM for Everyone: Representing the Underrepresented in Large Language Models [21.07409393578553]
  This thesis aims to bridge the gap in NLP research and development by focusing on underrepresented languages. A comprehensive evaluation of large language models (LLMs) is conducted to assess their capabilities in these languages. The proposed solutions cover cross-lingual continual instruction tuning, retrieval-based cross-lingual in-context learning, and in-context query alignment.
  arXiv  Detail & Related papers  (2024-09-20T20:53:22Z)
• 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, i.e., be crosslingual? This study evaluates 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.<n>Extensive experiments demonstrate that our multilingual feedback approach outperforms various strong baselines.<n>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)
• MindMerger: Efficient Boosting LLM Reasoning in non-English Languages [26.334092384176518]
  Reasoning capabilities are crucial for Large Language Models (LLMs) We propose MindMerger, which merges LLMs with the external language understanding capabilities from multilingual models. MindMerger consistently outperforms all baselines, especially in low-resource languages.
  arXiv  Detail & Related papers  (2024-05-27T17:41:54Z)
• 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. This work extends the evaluation from NLP tasks to real user queries. For culture-related tasks that need deep language understanding, prompting in the native language tends to be more promising.
  arXiv  Detail & Related papers  (2024-03-15T12:47:39Z)
• UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset [69.33424532827608]
  Open-source large language models (LLMs) have gained significant strength across diverse fields. In this work, we construct an open-source multilingual supervised fine-tuning dataset. The resulting UltraLink dataset comprises approximately 1 million samples across five languages.
  arXiv  Detail & Related papers  (2024-02-07T05:05:53Z)

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.