Beyond prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations (2022.emnlp-main)
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| Challenge: | Existing methods for zero-shot text classification involve heavy human engineering or complicated self-training pipelines. |
| Approach: | They propose to fit unlabeled text with a Bayesian Gaussian Mixture Model and use class names to cluster them. |
| Outcome: | The proposed approach outperforms prompt-based methods on topic and sentiment datasets and outperformed previous studies significantly on unbalanced datasets. |
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| Challenge: | Existing approaches to pre-trained language models require fine-tuning on labeled datasets or manually constructing proper prompts. |
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Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections (2021.findings-emnlp)
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| Challenge: | Large pre-trained language models (LMs) have a surprising ability to perform zero-shot learning. |
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ZeroDL: Zero-shot Distribution Learning for Text Clustering via Large Language Models (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have shown impressive performance on downstream tasks, but if they cannot be fully described in prompts, they could fail to perform the task. |
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| Challenge: | Experimental results show that pre-trained language models outperform standard prompt learning in zero-shot settings. |
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Zero-Shot Text Classification with Self-Training (2022.emnlp-main)
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| Challenge: | Recent advances in large pretrained language models have increased attention to zero-shot text classification. |
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| Challenge: | Recent work has obtained strong zero-shot results by prompting language models. |
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Label Agnostic Pre-training for Zero-shot Text Classification (2023.findings-acl)
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| Challenge: | Existing approaches to text classification assume a fixed set of labels . however, in real-world applications, there exists an infinite label space for describing a given text . |
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Zero-Shot Text Classification via Self-Supervised Tuning (2023.findings-acl)
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| Challenge: | Existing solutions to zero-shot text classification use pre-trained language models or large-scale annotated data. |
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Small Language Models Are Good Too: An Empirical Study of Zero-Shot Classification (2024.lrec-main)
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| Challenge: | Using small language models, we challenge the dominance of large models in text classification by prompting. |
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Language Models for Text Classification: Is In-Context Learning Enough? (2024.lrec-main)
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| Challenge: | Existing research on text classification models with prompts is limited in scale and lacks understanding of how these methods compare to more established methods. |
| Approach: | They compare the performance of large and smaller language models with prompts to achieve state-of-the-art performance in many NLP tasks. |
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