Quality-Aware Translation Tagging in Multilingual RAG system
Hoyeon Moon, Byeolhee Kim, Nikhil Verma

TL;DR
This paper introduces QTT-RAG, a method that evaluates and tags translation quality in multilingual retrieval-augmented generation, improving factual accuracy and naturalness in low-resource language QA tasks.
Contribution
QTT-RAG explicitly assesses translation quality along three dimensions and uses this metadata to enhance multilingual response generation without content alteration.
Findings
QTT-RAG outperforms baseline models in low-resource language QA benchmarks.
The approach preserves factual integrity and translation reliability.
Effective across multiple languages and model sizes.
Abstract
Multilingual Retrieval-Augmented Generation (mRAG) often retrieves English documents and translates them into the query language for low-resource settings. However, poor translation quality degrades response generation performance. Existing approaches either assume sufficient translation quality or utilize the rewriting method, which introduces factual distortion and hallucinations. To mitigate these problems, we propose Quality-Aware Translation Tagging in mRAG (QTT-RAG), which explicitly evaluates translation quality along three dimensions-semantic equivalence, grammatical accuracy, and naturalness&fluency-and attach these scores as metadata without altering the original content. We evaluate QTT-RAG against CrossRAG and DKM-RAG as baselines in two open-domain QA benchmarks (XORQA, MKQA) using six instruction-tuned LLMs ranging from 2.4B to 14B parameters, covering two low-resource…
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