Papers with fully supervised methods
Exploiting Language Model Prompts Using Similarity Measures: A Case Study on the Word-in-Context Task (2022.acl-short)
Copied to clipboard
| Challenge: | Existing few-shot approaches fail on the semantic distinction task of the Word-in-Context dataset. |
| Approach: | They propose a prompt-based approach which boosts few-shot performance to the level of fully supervised methods by using similarity metrics. |
| Outcome: | The proposed technique boosts few-shot performance to the level of fully supervised methods. |
Self-Supervised Test-Time Learning for Reading Comprehension (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent work on unsupervised question answering shows that models can be trained with procedurally generated question-answer pairs and achieve performance competitive with supervised methods. |
| Approach: | They propose a method that performs "test-time learning" on a given context . they use self-supervision to train models on synthetically generated question-answer pairs . |
| Outcome: | The proposed method outperforms current unsupervised methods and outperformed supervised methods. |
Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Experiments show that PICO span detection results achieve much higher results for recall when compared to fully supervised methods. |
| Approach: | They propose to extract and then normalise PICO information from clinical trial articles and use crowdsourced sentence-level annotations to detect spans. |
| Outcome: | The proposed method achieves much higher results for recall when compared to fully supervised methods with PICO sentence detection at least as good as human annotations. |
Is Compound Aspect-Based Sentiment Analysis Addressed by LLMs? (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Aspect-based sentiment analysis (ABSA) aims to predict aspect-based elements from text . large language models (LLMs) have impressive abilities in handling human instructions . |
| Approach: | They propose a framework to evaluate LLMs' ability to handle complex ABSA tasks . they use constrained prompts to automatically organize the returned predictions . |
| Outcome: | The proposed framework outperforms supervised methods in some cases, but it is still lacking in other areas. |