Benchmarking and Building Zero-Shot Hindi Retrieval Model with Hindi-BEIR and NLLB-E5
This addresses the need for robust retrieval systems for Hindi speakers by providing a benchmark and model, though it is incremental as it builds on existing multilingual approaches.
The authors tackled the lack of comprehensive benchmarks for Hindi information retrieval by introducing the Hindi-BEIR benchmark with 15 datasets across seven tasks, and they developed NLLB-E5, a zero-shot multilingual retrieval model that supports Hindi without training data, achieving competitive performance as evaluated on the benchmark.
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.