Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following Large Language Models

Core Concepts

A novel self-training framework, GenCo, that leverages the generative power of large language models (LLMs) to train a smaller and more adaptable language model for zero-shot text classification.

Abstract

The paper introduces a novel approach called Generation-driven Contrastive Self-Training (GenCo) that combines the language understanding ability of LLMs with the adaptability and efficiency of smaller models for zero-shot text classification.
Key highlights:

GenCo exploits the generative strengths of LLMs in two ways: 1) to enhance pseudo label prediction by generating multiple variations of the input text, and 2) to craft new training instances conditioned on the pseudo labels, ensuring the generated content is closely aligned with the assigned pseudo label.
This tackles the prevalent issue of mislabeling in self-training and reduces the dependency on large volumes of unlabeled text.
GenCo outperforms previous state-of-the-art methods when only limited (< 5% of original) in-domain text data is available.
The approach surpasses the performance of Alpaca-7B with human prompts, highlighting the potential of leveraging LLMs for self-training.
The authors provide theoretical analysis to support the effectiveness of the proposed contrastive loss for self-training.

Stats

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Quotes

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Key Insights Distilled From

Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM

by Ruohong Zhan... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2304.11872.pdf

Deeper Inquiries

How can the proposed GenCo framework be extended to other NLP tasks beyond text classification, such as question answering or dialogue systems?

The GenCo framework can be extended to other NLP tasks by adapting the concept of leveraging LLMs for data augmentation and self-training. For question answering tasks, the LLM can be used to generate additional context or possible answers to augment the input data. This augmented data can then be used to improve the performance of question answering models through self-training. Similarly, for dialogue systems, the LLM can generate diverse responses or conversational prompts to enrich the training data and enhance the dialogue generation capabilities of the system.

What are the potential limitations or drawbacks of relying on LLMs for data augmentation, and how can these be addressed?

One potential limitation of relying on LLMs for data augmentation is the computational resources required to generate large amounts of augmented data. LLMs are computationally expensive and may not be feasible for real-time or large-scale applications. Additionally, there is a risk of generating low-quality or irrelevant data, which can negatively impact the performance of the downstream models. To address these limitations, it is important to carefully control the generation process, validate the quality of the augmented data, and consider alternative data augmentation techniques that are more computationally efficient.

How might the insights from this work on leveraging LLM generation capabilities inform the design of more efficient and effective NLP models in the future?

The insights from leveraging LLM generation capabilities in the GenCo framework can inform the design of more efficient and effective NLP models by highlighting the importance of incorporating generative models in the training process. By using LLMs for data augmentation and self-training, models can benefit from the rich contextual information and diverse data generated by these large language models. This approach can lead to improved generalization, better performance on zero-shot tasks, and reduced dependency on large labeled datasets. Future NLP models can leverage these insights to enhance their training processes, improve their adaptability to new tasks, and achieve higher levels of performance across a range of NLP applications.