Papers with prompt-learning
OpenPrompt: An Open-source Framework for Prompt-learning (2022.acl-demo)
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| Challenge: | Prompt-learning is a new paradigm in natural language processing, adapting pre-trained language models to cloze-style prediction, autoregressive modeling, or sequence to sequence generation. |
| Approach: | They propose a framework for prompt-learning that integrates pre-trained language models with a unified framework. |
| Outcome: | The proposed framework is easy to use and flexible enough to integrate with other frameworks. |
Enhancing Cross-lingual Natural Language Inference by Prompt-learning from Cross-lingual Templates (2022.acl-long)
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| Challenge: | Existing methods for enhancing pre-trained cross-lingual language models with additional data are rare in practice, especially for low-resource languages. |
| Approach: | They propose a prompt-learning framework for enhancing cross-lingual natural language inference by constructing cloze-style questions through cross-linguistic templates. |
| Outcome: | The proposed framework significantly outperforms existing models under cross-lingual transfer settings. |
NSP-BERT: A Prompt-based Few-Shot Learner through an Original Pre-training Task —— Next Sentence Prediction (2022.coling-1)
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| Challenge: | Recent studies have shown that using prompts to utilize language models to perform downstream tasks is more effective than using token-level methods such as PET. |
| Approach: | They propose to use a BERT original pre-training task abandoned by RoBERTa and other models to construct a sentence-level prompt-based method that does not need to fix the length of the prompt or the position to be predicted. |
| Outcome: | The proposed method performs better than PET and EFL on a BERT pre-training task and is comparable to other prompt-based methods. |
LLMEmbed: Rethinking Lightweight LLM’s Genuine Function in Text Classification (2024.acl-long)
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| Challenge: | Recent attempts to improve text classification performance are based on heuristic Chain-of-Thought (CoT) LLMEmbed is a simple and effective transfer learning strategy that can be used to improve the performance of large language models. |
| Approach: | They propose a simple transfer learning strategy to improve text classification using heuristic Chain-of-Thought. |
| Outcome: | The proposed method achieves strong performance on publicly available datasets while using low training overhead. |
Prompt-learning for Fine-grained Entity Typing (2022.findings-emnlp)
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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. |