@inproceedings{agarwal-etal-2025-aligning-llms,
    title = "Aligning {LLM}s for Multilingual Consistency in Enterprise Applications",
    author = "Agarwal, Amit  and
      Meghwani, Hansa  and
      Patel, Hitesh Laxmichand  and
      Sheng, Tao  and
      Ravi, Sujith  and
      Roth, Dan",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.9/",
    pages = "117--137",
    ISBN = "979-8-89176-333-3",
    abstract = "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."
}