Papers with active learning framework
One Size Does Not Fit All: The Case for Personalised Word Complexity Models (2022.findings-naacl)
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| Challenge: | Complex word identification (CWI) aims to identify words in a text that are difficult for a reader to understand and therefore benefit from simplification. |
| Approach: | They propose to use a novel active learning framework to tailor models to individual readers and release a dataset of complexity annotations and models as a benchmark for further research. |
| Outcome: | The proposed model can be tailored to individual readers and released as a benchmark for future research. |
Thesis Proposal: Measuring Prejudice at Scale (2026.eacl-srw)
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| Challenge: | Existing methods for NLP fail to confirm construct validity, limiting the validity of the model. |
| Approach: | They propose to shift from categorical classification to comparative scaling of grounded constructs by using prompt optimization and distillation approaches. |
| Outcome: | The proposed pipeline is scalable for moving from categorical classification to theoretically grounded comparative measurement. |
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates (2021.eacl-main)
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Artem Shelmanov, Dmitri Puzyrev, Lyubov Kupriyanova, Denis Belyakov, Daniil Larionov, Nikita Khromov, Olga Kozlova, Ekaterina Artemova, Dmitry V. Dylov, Alexander Panchenko
| Challenge: | Annotating training data for sequence tagging of texts is usually very time-consuming . active learning can help to reduce the amount of annotation required to train a good model by multiple times . |
| Approach: | They are the first to thoroughly investigate active learning and transfer learning for natural language processing . they propose to combine active learning with active learning to improve model acquisition . |
| Outcome: | The proposed combination of active learning and Bayesian uncertainty estimation improves performance and reduces obstacles for applying it in practice. |
Hallucination Diversity-Aware Active Learning for Text Summarization (2024.naacl-long)
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| Challenge: | Existing methods for alleviating hallucinations require costly human annotations . Existing approaches focus on a specific type of hallucinism, which limits their effectiveness . |
| Approach: | They propose a method to detect hallucinations from errors in semantic frame, discourse and content verifiability in LLM summarization using HAllucination Diversity-Aware Sampling. |
| Outcome: | The proposed framework reduces the need for costly human annotations to correct hallucinations in LLM outputs. |