Zero-Shot Learners for Natural Language Understanding via a Unified Multiple-Choice Perspective

Zero-shot learning is an approach where models generalize to unseen tasks without direct training on them. We introduce the Unified Multiple-Choice (UniMC) framework, which is format-independent, compatible with various formats, and applicable to tasks like text classification and sentiment analysis. Furthermore, we design a two-stage tuning method, initially training on multiple-choice formats to develop format-agnostic capabilities, and subsequently enabling direct predictions on unseen tasks for zero-shot learning. Our methodology avoids issues in large-scale models like FLAN, enhancing generalization and reducing parameters. In experiments, UniMC shows State-of-the-Art (SOTA) performance across out-of-domain and in-domain benchmarks, with only 235M parameters, far fewer than previous methods. Moreover, the UniMC-Chinese model excels beyond human performance on benchmarks like EPRSTMT and CHID-FC, underscoring its generalization capacity across languages. Additionally, ablation experiments demonstrate the effectiveness of our design. The code and model weights are available at https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/unimc.

Automated summary

This paper introduces the Unified Multiple-Choice (UniMC) framework for zero-shot learning, which achieves outstanding performance on tasks like text classification and sentiment analysis. The two-stage tuning method enables direct predictions on unseen tasks and reduces parameters, avoiding issues in large-scale models. Experimental results demonstrate the State-of-the-Art (SOTA) performance of UniMC and its strong generalization capacity across languages.