Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E5

TL;DR

This paper introduces Hindi-BEIR, a comprehensive benchmark for Hindi retrieval tasks, and NLLB-E5, a zero-shot multilingual retrieval model that enhances Hindi information retrieval without requiring Hindi-specific training data.

Contribution

The paper presents the first Hindi-specific retrieval benchmark and a zero-shot model, advancing multilingual retrieval research for Hindi.

Findings

NLLB-E5 outperforms existing models on Hindi retrieval tasks.

Hindi-BEIR provides a diverse set of datasets for benchmarking.

Zero-shot approach effectively supports Hindi retrieval without Hindi training data.

Abstract

Given the large number of Hindi speakers worldwide, there is a pressing need for robust and efficient information retrieval systems for Hindi. Despite ongoing research, comprehensive benchmarks for evaluating retrieval models in Hindi are lacking. To address this gap, we introduce the Hindi-BEIR benchmark, comprising 15 datasets across seven distinct tasks. We evaluate state-of-the-art multilingual retrieval models on the Hindi-BEIR benchmark, identifying task and domain-specific challenges that impact Hindi retrieval performance. Building on the insights from these results, we introduce NLLB-E5, a multilingual retrieval model that leverages a zero-shot approach to support Hindi without the need for Hindi training data. We believe our contributions, which include the release of the Hindi-BEIR benchmark and the NLLB-E5 model, will prove to be a valuable resource for researchers and promote advancements in multilingual retrieval models.