Predicting and Explaining Cross-lingual Zero-shot and Few-shot Transfer in LLMs

Given a massively multilingual language models (MMLM), can we predict the accuracy of cross-lingual zero-shot and few-shot transfer for a task on target languages with little or no test data? This seemingly impossible task, if solved, can have several potential benefits. First, we could estimate the performance of a model even in languages where a test set is not available, and/or building one is difficult. Second, one can predict training data configurations that would give certain desired performance across a set of languages, and accordingly strategize data collection plans; this in turn can lead to linguistically fair MMLM-based models. Third, as a byproduct, we would know which factors influence cross-lingual transfer. In this talk, I will give an overview of Project LITMUS – Linguistically Informed Training and Testing of Multilingual Systems, where we build several ML models for performance prediction; besides their applications, I will discuss what we learn about the factors that influence cross-lingual transfer.

Post Talk Link:  Click Here 

Passcode: 8Tc0vHE@