Papers with unsupervised and supervised models
SKICSE: Sentence Knowable Information Prompted by LLMs Improves Contrastive Sentence Embeddings (2024.naacl-short)
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| Challenge: | Experimental results show that our model outperforms the previous state-of-the-art model GloVe on STS tasks. |
| Approach: | They propose a simple and effective prompt template that is able to obtain the knowable information of input sentences from LLMs. |
| Outcome: | The proposed model outperforms the previous state-of-the-art model PromptBERT on STS tasks. |
Unsupervised Summarization Re-ranking (2023.findings-acl)
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| Challenge: | Abstractive summarization models have been gaining popularity, but performance of unsupervised models still lags behind supervised models. |
| Approach: | They propose to re-rank summary candidates in an unsupervised manner to close the performance gap between unsupervised and supervised models. |
| Outcome: | The proposed model improves unsupervised models by up to 7.27% and ChatGPT by up 6.86% relative mean ROUGE across four widely-adopted summarization benchmarks. |
SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021.emnlp-main)
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| Challenge: | Existing methods for learning universal sentence embeddings are based on unsupervised approaches with only dropout as noise. |
| Approach: | They propose an unsupervised approach that takes an input sentence and predicts itself in a contrastive objective with only standard dropout used as noise. |
| Outcome: | The proposed framework performs on par with previous supervised approaches and can produce superior sentence embeddings from unlabeled or labeled data. |