Multilingual Retrieval Augmented Generation for Culturally-Sensitive Tasks: A Benchmark for Cross-lingual Robustness

• URL: http://arxiv.org/abs/2410.01171v2
• Date: Tue, 18 Feb 2025 18:32:25 GMT
• Title: Multilingual Retrieval Augmented Generation for Culturally-Sensitive Tasks: A Benchmark for Cross-lingual Robustness
• Authors: Bryan Li, Fiona Luo, Samar Haider, Adwait Agashe, Tammy Li, Runqi Liu, Muqing Miao, Shriya Ramakrishnan, Yuan Yuan, Chris Callison-Burch,
• Abstract summary: We introduce BordIRLines, a benchmark consisting of 720 territorial dispute queries paired with 14k Wikipedia documents across 49 languages. Our experiments reveal that retrieving multilingual documents best improves response consistency and decreases geopolitical bias over using purely in-language documents. Our further experiments and case studies investigate how cross-lingual RAG is affected by aspects from IR to document contents.
• Score: 30.00463676754559
• License:
• Abstract: The paradigm of retrieval-augmented generated (RAG) helps mitigate hallucinations of large language models (LLMs). However, RAG also introduces biases contained within the retrieved documents. These biases can be amplified in scenarios which are multilingual and culturally-sensitive, such as territorial disputes. In this paper, we introduce BordIRLines, a benchmark consisting of 720 territorial dispute queries paired with 14k Wikipedia documents across 49 languages. To evaluate LLMs' cross-lingual robustness for this task, we formalize several modes for multilingual retrieval. Our experiments on several LLMs reveal that retrieving multilingual documents best improves response consistency and decreases geopolitical bias over using purely in-language documents, showing how incorporating diverse perspectives improves robustness. Also, querying in low-resource languages displays a much wider variance in the linguistic distribution of response citations. Our further experiments and case studies investigate how cross-lingual RAG is affected by aspects from IR to document contents. We release our benchmark and code to support further research towards ensuring equitable information access across languages at https://huggingface.co/datasets/borderlines/bordirlines.

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