Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization

TL;DR

This paper introduces UDIT, a method that enhances instruction tuning for zero-shot cross-task generalization by leveraging unlabeled data to generate pseudo-labeled examples, reducing reliance on human-annotated samples.

Contribution

The paper proposes UDIT, a novel approach that improves instruction tuning by utilizing unlabeled data to create pseudo-labeled instructions, enabling better generalization with less labeled data.

Findings

UDIT outperforms standard instruction tuning across multiple datasets

Enlarging the number of instructions improves zero-shot performance

Unlabeled data effectively augment instruction tuning, reducing dependence on labeled data

Abstract

Training language models to learn from human instructions for zero-shot cross-task generalization has attracted much attention in NLP communities. Recently, instruction tuning (IT), which fine-tunes a pre-trained language model on a massive collection of tasks described via human-craft instructions, has been shown effective in instruction learning for unseen tasks. However, IT relies on a large amount of human-annotated samples, which restricts its generalization. Unlike labeled data, unlabeled data are often massive and cheap to obtain. In this work, we study how IT can be improved with unlabeled data. We first empirically explore the IT performance trends versus the number of labeled data, instructions, and training tasks. We find it critical to enlarge the number of training instructions, and the instructions can be underutilized due to the scarcity of labeled data. Then, we propose Unlabeled Data Augmented Instruction Tuning (UDIT) to take better advantage of the instructions during IT by constructing pseudo-labeled data from unlabeled plain texts. We conduct extensive experiments to show UDIT's effectiveness in various scenarios of tasks and datasets. We also comprehensively analyze the key factors of UDIT to investigate how to better improve IT with unlabeled data. The code is publicly available at https://github.com/thu-coai/UDIT.