Language Drift in Multilingual Retrieval-Augmented Generation: Characterization and Decoding-Time Mitigation

• URL: http://arxiv.org/abs/2511.09984v1
• Date: Fri, 14 Nov 2025 01:23:50 GMT
• Title: Language Drift in Multilingual Retrieval-Augmented Generation: Characterization and Decoding-Time Mitigation
• Authors: Bo Li, Zhenghua Xu, Rui Xie,
• Abstract summary: We study output language drift in multilingual RAG across multiple datasets, languages, and LLM backbones.<n>Our experiments reveal that the drift results from decoder-level collapse, where dominant token distributions and high-frequency English patterns dominate the intended generation language.<n>We propose Soft Constrained Decoding (SCD), a lightweight, training-free decoding strategy that gently steers generation toward the target language.
• Score: 11.110312833458421
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multilingual Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to perform knowledge-intensive tasks in multilingual settings by leveraging retrieved documents as external evidence. However, when the retrieved evidence differs in language from the user query and in-context exemplars, the model often exhibits language drift by generating responses in an unintended language. This phenomenon is especially pronounced during reasoning-intensive decoding, such as Chain-of-Thought (CoT) generation, where intermediate steps introduce further language instability. In this paper, we systematically study output language drift in multilingual RAG across multiple datasets, languages, and LLM backbones. Our controlled experiments reveal that the drift results not from comprehension failure but from decoder-level collapse, where dominant token distributions and high-frequency English patterns dominate the intended generation language. We further observe that English serves as a semantic attractor under cross-lingual conditions, emerging as both the strongest interference source and the most frequent fallback language. To mitigate this, we propose Soft Constrained Decoding (SCD), a lightweight, training-free decoding strategy that gently steers generation toward the target language by penalizing non-target-language tokens. SCD is model-agnostic and can be applied to any generation algorithm without modifying the architecture or requiring additional data. Experiments across three multilingual datasets and multiple typologically diverse languages show that SCD consistently improves language alignment and task performance, providing an effective and generalizable solution in multilingual RAG.

Related papers

• Language-Coupled Reinforcement Learning for Multilingual Retrieval-Augmented Generation [73.54930910609328]
  We propose LcRL, a multilingual search-augmented reinforcement learning framework.<n>LcRL integrates a language-coupled Group Relative Policy Optimization into the policy and reward models.<n>We adopt the language-coupled group sampling in the rollout module to reduce knowledge bias, and regularize an auxiliary anti-consistency penalty in the reward models to mitigate the knowledge conflict.
  arXiv  Detail & Related papers  (2026-01-21T11:32:32Z)
• Tracing Multilingual Representations in LLMs with Cross-Layer Transcoders [51.380449540006985]
  Large Language Models (LLMs) can process many languages, yet how they internally represent this diversity remains unclear.<n>Do they form shared multilingual representations with language-specific decoding, and if so, why does performance still favor the dominant training language?<n>We analyze their internal mechanisms using cross-layer transcoders (CLT) and attribution graphs.
  arXiv  Detail & Related papers  (2025-11-13T22:51:06Z)
• Beyond Language Barriers: Multi-Agent Coordination for Multi-Language Code Generation [8.896718697354187]
  XL-CoGen produces high-quality code across multiple programming languages.<n>It integrates intermediate representation, code generation, translation, and automated repair.
  arXiv  Detail & Related papers  (2025-09-24T09:18:08Z)
• DIVERS-Bench: Evaluating Language Identification Across Domain Shifts and Code-Switching [8.14614722074297]
  Language Identification (LID) is a core task in multilingual NLP, yet current systems often overfit to clean, monolingual data.<n>This work introduces DIVERS-BENCH, a comprehensive evaluation of state-of-the-art LID models across diverse domains.<n>Our findings reveal that while models achieve high accuracy on curated datasets, performance degrades sharply on noisy and informal inputs.
  arXiv  Detail & Related papers  (2025-09-22T13:32:31Z)
• MrGuard: A Multilingual Reasoning Guardrail for Universal LLM Safety [56.77103365251923]
  Large Language Models (LLMs) are susceptible to adversarial attacks such as jailbreaking.<n>This vulnerability is exacerbated in multilingual settings, where multilingual safety-aligned data is often limited.<n>We introduce a multilingual guardrail with reasoning for prompt classification.
  arXiv  Detail & Related papers  (2025-04-21T17:15:06Z)
• Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task [89.45111250272559]
  Retrieval-augmented generation (RAG) has become a cornerstone of contemporary NLP.<n>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)
• Investigating Language Preference of Multilingual RAG Systems [4.438698005789677]
  MRAG systems struggle with retrieving relevant information due to linguistic variations between queries and documents.<n>We propose Dual Knowledge Multilingual RAG (DKM-RAG), a framework that fuses translated multilingual passages with complementary model knowledge.<n> Empirical results demonstrate that DKM-RAG mitigates language preference in generation and enhances performance across diverse linguistic settings.
  arXiv  Detail & Related papers  (2025-02-16T15:54:05Z)
• xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning [36.34986831526529]
  Chain-of-thought (CoT) has emerged as a powerful technique to elicit reasoning in large language models. We propose a cross-lingual instruction fine-tuning framework (xCOT) to transfer knowledge from high-resource languages to low-resource languages.
  arXiv  Detail & Related papers  (2024-01-13T10:53:53Z)
• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)
• Overcoming Language Disparity in Online Content Classification with Multimodal Learning [22.73281502531998]
  Large language models are now the standard to develop state-of-the-art solutions for text detection and classification tasks. The development of advanced computational techniques and resources is disproportionately focused on the English language. We explore the promise of incorporating the information contained in images via multimodal machine learning.
  arXiv  Detail & Related papers  (2022-05-19T17:56:02Z)
• Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding [90.87454350016121]
  We develop novel code-switching schemes to generate hard negative examples for contrastive learning at all levels. We develop a label-aware joint model to leverage label semantics for cross-lingual knowledge transfer.
  arXiv  Detail & Related papers  (2022-05-07T13:44:28Z)
• XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning [68.57658225995966]
  Cross-lingual Choice of Plausible Alternatives (XCOPA) is a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages. We evaluate a range of state-of-the-art models on this novel dataset, revealing that the performance of current methods falls short compared to translation-based transfer.
  arXiv  Detail & Related papers  (2020-05-01T12:22:33Z)

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.