Ning Ding, Yulin Chen, Xu Han, Guangwei Xu, Xiaobin Wang, Pengjun Xie, Haitao Zheng, Zhiyuan Liu, Juanzi Li, Hong-Gee Kim
| Challenge: | Extensive experiments on fine-grained entity typing under fully supervised, few-shot, and zero-shot settings show the effectiveness of prompt-learning. |
| Approach: | They propose a prompt-learning pipeline that stimulates versatile knowledge of pre-trained language models (PLMs) by constructing entity-oriented verbalizers and templates and conducting masked language modeling. |
| Outcome: | The proposed approach can be applied to fine-grained entity typing in fully supervised, few-shot, and zero-shot scenarios. |
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| Challenge: | Named Entity Recognition (NER) is a low-resource task that requires supervised learning, but practical scenarios lack annotated data. |
| Approach: | They propose an Evolving Prompts framework that guides the model to better address these issues through continuous prompt refinement. |
| Outcome: | The proposed framework shows consistent performance improvements on four benchmarks. |
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models (2022.findings-acl)
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| Challenge: | Prompting language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning. |
| Approach: | They propose to fine tune masked language models with training examples and task descriptions to reduce prompt engineering by using null prompts. |
| Outcome: | The proposed prompts can be used to improve few-shot learning by finetuning only the bias terms while updating only 0.1% of the parameters. |
Prompt-free and Efficient Few-shot Learning with Language Models (2022.acl-long)
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Rabeeh Karimi Mahabadi, Luke Zettlemoyer, James Henderson, Lambert Mathias, Marzieh Saeidi, Veselin Stoyanov, Majid Yazdani
| Challenge: | Existing methods for few-shot fine-tuning of pretrained language models require carefully engineered prompts and verbalizers to convert inputs into a cloze-format that the PLM can score. |
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PromptNER: Prompt Locating and Typing for Named Entity Recognition (2023.acl-long)
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Yongliang Shen, Zeqi Tan, Shuhui Wu, Wenqi Zhang, Rongsheng Zhang, Yadong Xi, Weiming Lu, Yueting Zhuang
| Challenge: | Existing methods for prompt learning require a multi-round prompting manner and require elaborate templates. |
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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. |
Pre-trained Language Models Can be Fully Zero-Shot Learners (2023.acl-long)
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| Challenge: | Existing approaches to pre-trained language models require fine-tuning on labeled datasets or manually constructing proper prompts. |
| Approach: | They propose a nonparametric prompting PLM for fully zero-shot language understanding . they compare it to previous methods for text classification and text entailment . |
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Making Pre-trained Language Models Better Few-shot Learners (2021.acl-long)
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| Challenge: | Recent studies show that the GPT-3 model can perform few-shots on language understanding tasks with a natural-language prompt and a few task demonstrations. |
| Approach: | They propose a technique for fine-tuning language models using a few examples . they propose LM-BFF, which uses prompt-based fine-uning and a pipeline for automating prompt generation . |
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DSP: Discriminative Soft Prompts for Zero-Shot Entity and Relation Extraction (2023.findings-acl)
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| Challenge: | Prompt-based methods have shown their efficacy in transferring general knowledge within pre-trained language models (PLMs) however, when applied to zero-shot entity and relation extraction, they struggle with the limited coverage of verbalizers to labels and the slow inference speed. |
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Prompt Tuning for Discriminative Pre-trained Language Models (2022.findings-acl)
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Yuan Yao, Bowen Dong, Ao Zhang, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Leyu Lin, Maosong Sun, Jianyong Wang
| Challenge: | Recent studies have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing tasks. |
| Approach: | They propose a prompt tuning framework that reformulates NLP tasks into a discriminative language modeling problem. |
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PromptDA: Label-guided Data Augmentation for Prompt-based Few Shot Learners (2023.eacl-main)
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| Challenge: | Existing studies on prompt-based few-shot tuning focus on deriving proper label words with a verbalizer or generating prompt templates to elicit semantics from PLMs. |
| Approach: | They propose a framework that leverages label semantics for prompt-based tuning. |
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