When Retrieval Hurts: Evidence Utilization, Script Fidelity, and Knowledge Conflicts in Multilingual RAG

2026-06-26 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

An extensive empirical study investigates why retrieval-augmented generation (RAG) often degrades performance in multilingual question answering, despite high retrieval quality. This research compares RAG against a non-RAG baseline across 21 typologically diverse languages, utilizing 5 leading LLMs and 5 prompting strategies with multiple retrieval configurations. The study identifies a significant "evidence utilization gap," where LLMs fail to effectively leverage retrieved evidence, leading to underperformance despite high retrieval hit rates. It highlights script fidelity as a critical factor, observing substantial performance drops and increased hallucinations in non-Latin-script languages without proper grounding. The analysis also introduces lightweight inference-time metrics to characterize retrieval usage and conflict patterns, examines generator language preferences, and details how prompting strategies impact language families and script types.

Key takeaway

For NLP Engineers optimizing multilingual RAG systems, you must move beyond solely evaluating retrieval quality. Your focus should shift to assessing the LLM's actual evidence utilization, especially for non-Latin script languages where proper grounding is critical to prevent performance degradation and hallucinations. Systematically analyze how prompting strategies interact with different language families and script types to detect and resolve knowledge conflicts effectively.

Key insights

Multilingual RAG performance degrades due to an "evidence utilization gap" and script fidelity issues, particularly for non-Latin scripts.

Principles

• RAG performance can degrade despite high retrieval quality.
• Script fidelity is crucial for multilingual RAG.
• LLMs often underutilize retrieved evidence.

Method

The study proposes lightweight inference-time metrics to characterize retrieval usage and conflict patterns, alongside mechanisms for effective conflict detection and resolution in multilingual RAG.

In practice

• Ground non-Latin script languages to prevent hallucinations.
• Evaluate LLM evidence utilization, not just retrieval quality.
• Optimize prompting for specific language families/scripts.

Topics

• Multilingual RAG
• Large Language Models
• Script Fidelity
• Knowledge Conflicts
• Prompting Strategies
• Evidence Utilization

Best for: Research Scientist, AI Architect, AI Engineer, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.