Impact of Artificially Code-Switched Data Ratios on Multilingual Model Inference Performance in Zero-Shot Cross-Lingual Reasoning

Transferring information retrieval (IR) models from a high-resource language (typically English) to other languages in a zero-shot fashion has become a widely adopted approach. In this work, we show that the effectiveness of zero-shot rankers diminishes when queries and documents are present in different languages. Motivated by this, we propose to train ranking models on artificially code-switched data instead, which we generate by utilizing bilingual lexicons. To this end, we experiment with lexicons induced from (1) cross-lingual word embeddings and (2) parallel Wikipedia page titles. We use

Research goal: What is the effect of varying the ratio of artificially code-switched data (e.g., 10%, 30%, 50%) on the inference latency and throughput of multilingual models when evaluated on the QUAIL benchmark for zero-shot cross-lingual reasoning tasks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.