Papers with natural language supervision
CLIPText: A New Paradigm for Zero-shot Text Classification (2023.findings-acl)
Copied to clipboard
| Challenge: | Experimental results show that CLIP can be applied to zero-shot text classification tasks. |
| Approach: | They propose a CLIP model for zero-shot text classification that integrates prompt into CLIPText to better derive knowledge from CLIP. |
| Outcome: | The proposed model can be applied to a text-image matching problem and show that it can be used for language tasks. |
Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is a language understanding task that requires large amounts of in-domain labeled data to perform well. |
| Approach: | They propose a framework which learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data. |
| Outcome: | The proposed method brings 10%, 23% and 26% improvements over baselines in few-shot learning, domain transfer and zero-shot settings respectively. |
Learning to Learn Semantic Parsers from Natural Language Supervision (D18-1)
Copied to clipboard
| Challenge: | Existing logical forms require a user to be familiar with the underlying structure to learn a semantic parser. |
| Approach: | They propose a method for training semantic parsers from natural language feedback . they use natural language inputs to parse feedback to leverage it as a form of supervision . |
| Outcome: | The proposed algorithm learns a semantic parser from users’ corrections expressed in natural language. |
LaSQuE: Improved Zero-Shot Classification from Explanations Through Quantifier Modeling and Curriculum Learning (2023.findings-acl)
Copied to clipboard
| 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. |
Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study (2025.findings-emnlp)
Copied to clipboard
Yujun Zhou, Jiayi Ye, Zipeng Ling, Yufei Han, Yue Huang, Haomin Zhuang, Zhenwen Liang, Kehan Guo, Taicheng Guo, Xiangqi Wang, Xiangliang Zhang
| Challenge: | Existing benchmarks that rely on final-answer accuracy fail to capture the quality of the reasoning process. |
| Approach: | They propose a fine-grained evaluation framework that assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing. |
| Outcome: | The proposed framework assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing. |