Cross-Domain Evaluation for Multi-Task Learning in NLP: A Unified Framework for Generalization and Robustness

Abstract

Natural Language Processing (NLP) models have achieved significant breakthroughs on individual tasks, but their generalization across diverse domains remains a challenge. This work introduces a novel approach to enhancing model robustness and generalization by incorporating cross-domain evaluation within multi-task learning. We combine three distinctive datasets-WikiText-103, OpenWebText, and DROP-to propose a unified framework that facilitates evaluation across language modeling, conversational processing, and complex reasoning. Our key contributions include: (1) a new cross-domain evaluation strategy that assesses both taskspecific performance and generalization across domains; (2) an analysis of how multi-task learning across heterogeneous datasets improves generalization to unseen tasks; and (3) empirical results showing that models trained on multiple datasets exhibit superior performance and robustness compared to models trained on single-task datasets. We demonstrate that this approach provides a promising direction for developing more generalizable models in NLP, capable of handling a wider range of real-world tasks with high accuracy.

Keywords: Cross-Domain Evaluation, Multi-Task Learning, Natural Language Processing (NLP)Generalization, Robustness, Language Modeling, Conversational Processing, Complex, Reasoning, GPT-2, T5 Model, WikiText-103, OpenWebText, DROP Dataset, Multi-Domain, NLP Applications, Tokenization and Preprocessing