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

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

URL: http://arxiv.org/abs/2304.11872v2
Date: Mon, 15 Apr 2024 02:40:54 GMT
Title: Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM
Authors: Ruohong Zhang, Yau-Shian Wang, Yiming Yang,
Abstract summary: We introduce a novel method, namely GenCo, which leverages the strong generative power of large language models to assist in training a smaller language model. In our method, an LLM plays an important role in the self-training loop of a smaller model in two important ways. It helps crafting additional high-quality training pairs, by rewriting input texts conditioned on predicted labels.
Score: 31.25193238045053
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The remarkable performance of large language models (LLMs) in zero-shot language understanding has garnered significant attention. However, employing LLMs for large-scale inference or domain-specific fine-tuning requires immense computational resources due to their substantial model size. To overcome these limitations, we introduce a novel method, namely GenCo, which leverages the strong generative power of LLMs to assist in training a smaller and more adaptable language model. In our method, an LLM plays an important role in the self-training loop of a smaller model in two important ways. Firstly, the LLM is used to augment each input instance with a variety of possible continuations, enriching its semantic context for better understanding. Secondly, it helps crafting additional high-quality training pairs, by rewriting input texts conditioned on predicted labels. This ensures the generated texts are highly relevant to the predicted labels, alleviating the prediction error during pseudo-labeling, while reducing the dependency on large volumes of unlabeled text. In our experiments, GenCo outperforms previous state-of-the-art methods when only limited ($<5\%$ of original) in-domain text data is available. Notably, our approach surpasses the performance of Alpaca-7B with human prompts, highlighting the potential of leveraging LLM for self-training.

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