Aligning LLMs for Multilingual Consistency in Enterprise Applications
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912669604249600 |
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| author | Agarwal, Amit Meghwani, Hansa Patel, Hitesh Laxmichand Sheng, Tao Ravi, Sujith Roth, Dan |
| author_facet | Agarwal, Amit Meghwani, Hansa Patel, Hitesh Laxmichand Sheng, Tao Ravi, Sujith Roth, Dan |
| contents | Large language models (LLMs) remain unreliable for global enterprise applications due to substantial performance gaps between high-resource and mid/low-resource languages, driven by English-centric pretraining and internal reasoning biases. This inconsistency undermines customer experience and operational reliability in multilingual settings such as customer support, content moderation, and information retrieval. Even with advanced Retrieval-Augmented Generation (RAG) systems, we observe up to an 29% accuracy drop in non-English languages compared to English. We propose a practical, batch-wise alignment strategy for fine-tuning LLMs, leveraging semantically equivalent multilingual data in each training batch to directly align model outputs across languages. This approach improves non-English accuracy by up to 23.9% without compromising English performance, model reasoning, or retrieval quality. Our method is simple to implement, scalable, and integrates seamlessly with existing LLM training & deployment pipelines, enabling more robust and equitable multilingual AI solutions in industry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23659 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Aligning LLMs for Multilingual Consistency in Enterprise Applications Agarwal, Amit Meghwani, Hansa Patel, Hitesh Laxmichand Sheng, Tao Ravi, Sujith Roth, Dan Computation and Language Artificial Intelligence 68T05, 68T50, 68Q25 I.2.7; I.5.1; I.2.8 Large language models (LLMs) remain unreliable for global enterprise applications due to substantial performance gaps between high-resource and mid/low-resource languages, driven by English-centric pretraining and internal reasoning biases. This inconsistency undermines customer experience and operational reliability in multilingual settings such as customer support, content moderation, and information retrieval. Even with advanced Retrieval-Augmented Generation (RAG) systems, we observe up to an 29% accuracy drop in non-English languages compared to English. We propose a practical, batch-wise alignment strategy for fine-tuning LLMs, leveraging semantically equivalent multilingual data in each training batch to directly align model outputs across languages. This approach improves non-English accuracy by up to 23.9% without compromising English performance, model reasoning, or retrieval quality. Our method is simple to implement, scalable, and integrates seamlessly with existing LLM training & deployment pipelines, enabling more robust and equitable multilingual AI solutions in industry. |
| title | Aligning LLMs for Multilingual Consistency in Enterprise Applications |
| topic | Computation and Language Artificial Intelligence 68T05, 68T50, 68Q25 I.2.7; I.5.1; I.2.8 |
| url | https://arxiv.org/abs/2509.23659 |