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

Abstract: The paradigm of retrieval-augmented generated (RAG) helps mitigate hallucinations of 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. We thus introduce BordIRLines, a dataset of territorial disputes paired with retrieved Wikipedia documents, across 49 languages. We evaluate the cross-lingual robustness of this RAG setting by formalizing several modes for multilingual retrieval. Our experiments on several LLMs show that incorporating perspectives from diverse languages can in fact improve robustness; retrieving multilingual documents best improves response consistency and decreases geopolitical bias over RAG with purely in-language documents. We also consider how RAG responses utilize presented documents, finding a much wider variance in the linguistic distribution of response citations, when querying in low-resource languages. Our further analyses investigate the various aspects of a cross-lingual RAG pipeline, from retrieval to document contents. We release our benchmark and code to support continued research towards equitable information access across languages at https://huggingface.co/datasets/borderlines/bordirlines.

• The paper introduces BordIRlines, a dataset designed to evaluate multilingual retrieval-augmented generation systems and mitigate LLM biases.
• It employs diverse Wikipedia sources and tests retrieval models like mDPR, COLBERT, BM25, and BGE M3 to assess document relevance across languages.
• The findings reveal significant inconsistencies in cross-lingual outputs, emphasizing the need for robust frameworks to balance language-specific biases.

Analysis of "BordIRlines: A Dataset for Evaluating Cross-lingual Retrieval-Augmented Generation"

The paper "BordIRlines: A Dataset for Evaluating Cross-lingual Retrieval-Augmented Generation" addresses the complexities of implementing retrieval-augmented generation (RAG) systems in multilingual environments. The authors introduce BordIRlines, a dataset focused on geopolitical disputes, aiming to enhance the robustness of RAG systems in cross-lingual contexts.

Research Context and Objectives

This research emerges from the ongoing challenges faced by LLMs, particularly in handling hallucinations and biases. Retrieval-augmented generation is proposed as a solution to ground LLM responses in factually accurate contexts. However, RAG introduces bias concerning the selection and weighting of the information source, especially when dealing with multilingual data.

With BordIRlines, the authors investigate geopolitical biases at the intersection of linguistic and cultural boundaries. The dataset is constructed from Wikipedia pages encompassing various languages and geopolitical perspectives. The main focus is to explore how differing contexts in multiple languages can influence LLM outputs and to examine the consistency of responses across language variations.

Methodology

The core contribution is the BordIRlines dataset, designed to test the resilience of RAG systems in a multilingual setting. The dataset involves geopolitical questions requiring a nuanced understanding due to multilingual and culturally diverse inputs. It pulls from Wikipedia articles corresponding to queries about territorial disputes, ensuring multiple perspectives by considering claimant countries and relevant languages.

The authors employ several retrieval models, including mDPR, COLBERT, BM25, and BGE M3, using both dense and sparse representations to optimize document relevance retrieval. Ablation studies are conducted to scrutinize how variations in contextual document compositions affect LLM responses.

Key Findings

The study finds that RAG systems face significant challenges in cross-lingual scenarios, often exhibiting inconsistencies when needing to reconcile conflicting information from different language sources. Two case studies illustrate these challenges, showing how altering linguistic contexts can lead to divergent LLM outputs.

The results emphasize the critical issue of consistency across languages and highlight the persistent biases in information retrieval and selection. When provided with multilingual contexts, the models' responses were notably influenced by the dominant language in the dataset.

Implications and Future Directions

The implications of this research are profound, both practically and theoretically. On a practical level, the findings suggest that developing robust RAG systems capable of handling multi-perspective, multilingual contexts is essential for ensuring unbiased and accurate outputs. Theoretical implications call for deeper exploration into the weighting of diverse language sources and the architectural adjustments required to mitigate informational bias in RAG systems.

Future research directions proposed include enhancing the framework for information balance in RAG processes and expanding beyond the Wikipedia domain to incorporate more varied and possibly biased sources. Additionally, more extensive human annotation to assess relevance and bias in retrieved passages is recommended.

Conclusion

"BordIRlines: A Dataset for Evaluating Cross-lingual Retrieval-Augmented Generation" is a significant contribution to the domain of RAG and LLM research, particularly in its exploration of cross-linguality. While the dataset and experiments underline the progress needed to address linguistic biases, they also propose a path forward to more inclusive and nuanced AI systems. The public availability of their tools and dataset invites further inquiry, promising advancement in this critical aspect of AI development.