Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task

• URL: http://arxiv.org/abs/2504.03616v1
• Date: Fri, 04 Apr 2025 17:35:43 GMT
• Title: Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task
• Authors: Leonardo Ranaldi, Barry Haddow, Alexandra Birch,
• Abstract summary: 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.
• Score: 73.35882908048423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-augmented generation (RAG) has become a cornerstone of contemporary NLP, enhancing large language models (LLMs) by allowing them to access richer factual contexts through in-context retrieval. While effective in monolingual settings, especially in English, its use in multilingual tasks remains unexplored. This paper investigates the effectiveness of RAG across multiple languages by proposing novel approaches for multilingual open-domain question-answering. We evaluate the performance of various multilingual RAG strategies, including question-translation (tRAG), which translates questions into English before retrieval, and Multilingual RAG (MultiRAG), where retrieval occurs directly across multiple languages. Our findings reveal that tRAG, while useful, suffers from limited coverage. In contrast, MultiRAG improves efficiency by enabling multilingual retrieval but introduces inconsistencies due to cross-lingual variations in the retrieved content. To address these issues, we propose Crosslingual RAG (CrossRAG), a method that translates retrieved documents into a common language (e.g., English) before generating the response. Our experiments show that CrossRAG significantly enhances performance on knowledge-intensive tasks, benefiting both high-resource and low-resource languages.

Related papers

• MR. Guard: Multilingual Reasoning Guardrail using Curriculum Learning [56.79292318645454]
  Large Language Models (LLMs) are susceptible to adversarial attacks such as jailbreaking. This vulnerability is exacerbated in multilingual setting, where multilingual safety-aligned data are often limited. We propose an approach to build a multilingual guardrail with reasoning.
  arXiv  Detail & Related papers  (2025-04-21T17:15:06Z)
• 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.<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)
• Demystifying Multilingual Chain-of-Thought in Process Reward Modeling [71.12193680015622]
  We tackle the challenge of extending process reward models (PRMs) to multilingual settings.<n>We train multilingual PRMs on a dataset spanning seven languages, which is translated from English.<n>Our results highlight the sensitivity of multilingual PRMs to both the number of training languages and the volume of English data.
  arXiv  Detail & Related papers  (2025-02-18T09:11:44Z)
• 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, 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)
• Not All Languages are Equal: Insights into Multilingual Retrieval-Augmented Generation [38.631934251052485]
  We evaluate six multilingual RALMs using our benchmark to explore the challenges of multilingual RALMs. High-resource languages stand out in Monolingual Knowledge Extraction. Indo-European languages lead RALMs to provide answers directly from documents. English benefits from RALMs' selection bias and speaks louder in multilingual knowledge selection.
  arXiv  Detail & Related papers  (2024-10-29T11:53:19Z)
• Multilingual Retrieval Augmented Generation for Culturally-Sensitive Tasks: A Benchmark for Cross-lingual Robustness [30.00463676754559]
  We introduce BordIRLines, a benchmark consisting of 720 territorial dispute queries paired with 14k Wikipedia documents across 49 languages.<n>Our experiments reveal that retrieving multilingual documents best improves response consistency and decreases geopolitical bias over using purely in-language documents.<n>Our further experiments and case studies investigate how cross-lingual RAG is affected by aspects from IR to document contents.
  arXiv  Detail & Related papers  (2024-10-02T01:59:07Z)
• 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.<n>But can these models relate corresponding concepts across languages, i.e., be crosslingual?<n>This study evaluates state-of-the-art LLMs on inherently crosslingual tasks.
  arXiv  Detail & Related papers  (2024-06-23T15:15:17Z)
• Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog [67.20796950016735]
  Multi2WOZ dataset spans four typologically diverse languages: Chinese, German, Arabic, and Russian. We introduce a new framework for multilingual conversational specialization of pretrained language models (PrLMs) that aims to facilitate cross-lingual transfer for arbitrary downstream TOD tasks. Our experiments show that, in most setups, the best performance entails the combination of (I) conversational specialization in the target language and (ii) few-shot transfer for the concrete TOD task.
  arXiv  Detail & Related papers  (2022-05-20T18:35:38Z)
• One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval [39.061900747689094]
  CORA is a Cross-lingual Open-Retrieval Answer Generation model. It can answer questions across many languages even when language-specific annotated data or knowledge sources are unavailable.
  arXiv  Detail & Related papers  (2021-07-26T06:02:54Z)
• Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension [86.1617182312817]
  We propose two auxiliary tasks in the fine-tuning stage to create additional phrase boundary supervision. A mixed Machine Reading task, which translates the question or passage to other languages and builds cross-lingual question-passage pairs. A language-agnostic knowledge masking task by leveraging knowledge phrases mined from web.
  arXiv  Detail & Related papers  (2020-04-29T10:44:00Z)
• XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization [128.37244072182506]
  Cross-lingual TRansfer Evaluation of Multilinguals XTREME is a benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks. We demonstrate that while models tested on English reach human performance on many tasks, there is still a sizable gap in the performance of cross-lingually transferred models.
  arXiv  Detail & Related papers  (2020-03-24T19:09:37Z)

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.