Papers with zero-shot classifiers
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. |
| Approach: | They propose a plug-and-play method to bridge this gap by requiring only class names along with an unlabeled dataset. |
| Outcome: | The proposed model can be trained on a natural language inference dataset and performs on dozens of unseen tasks without the need for domain expertise or trial and error. |
Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations (2025.naacl-long)
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| Challenge: | Prior work has focused on using large language models to simulate human behaviors . but, LLMs are known to generate erroneous, stereotypical, or overconfident answers . |
| Approach: | They propose to specialize large language models for simulating survey response distributions by first-token probabilities. |
| Outcome: | The proposed model outperforms other methods and zero-shot classifiers on unseen questions, countries, and a completely unseened survey. |
CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations (2022.acl-long)
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| Challenge: | Supervised learning has traditionally focused on inductive learning by looking at labeled examples of a task. |
| Approach: | They propose a benchmark for Classifier Learning Using natural language ExplanationS that provides natural language supervision over structured data and entailment-based models that learn from explanations. |
| Outcome: | The proposed model generalizes 18% better (relative) on novel tasks than a baseline that does not use explanations. |
LaSQuE: Improved Zero-Shot Classification from Explanations Through Quantifier Modeling and Curriculum Learning (2023.findings-acl)
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| Challenge: | Several recent approaches have explored training machine learning models via natural language supervision, but they fail to leverage linguistic quantifiers and mimic humans in compositionally learning complex tasks. |
| Approach: | They propose a method that can learn zero-shot classifiers from language explanations by using three new strategies: (1) modeling the semantics of linguistic quantifiers in explanations; (2) aggregating information from multiple explanations using an attention-based mechanism; (3) model training via curriculum learning. |
| Outcome: | The proposed method outperforms previous work showing an absolute gain of up to 7% in generalizing to unseen real-world classification tasks. |
PyRater: A Python Toolkit for Annotation Analysis (2024.lrec-main)
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| Challenge: | PyRater is an open-source Python toolkit for analysing corpora annotations. |
| Approach: | They propose to use PyRater to analyse corpora annotations. |
| Outcome: | The proposed model can be used to identify the best annotations and retrieve the gold standard. |