Papers with EAR
Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists (2022.findings-acl)
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| Challenge: | E.g., neural hate speech detection models are strongly influenced by identity terms like gay, or women, resulting in false positives, severe unintended bias, and lower performance. |
| Approach: | They propose a knowledge-free Entropy-based Attention Regularization (EAR) approach to discourage overfitting to training-specific terms. |
| Outcome: | The proposed model matches or exceeds state-of-the-art performance for hate speech classification and bias metrics on three benchmark corpora in English and Italian. |
Analyze, Generate and Refine: Query Expansion with LLMs for Zero-Shot Open-Domain QA (2024.findings-acl)
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| Challenge: | Existing methods like GAR and EAR rely heavily on supervised training and struggle to maintain effectiveness across domains and datasets. |
| Approach: | They propose a QE approach based on a three-step prompting strategy to enhance query expansion by broadening the scope of queries with additional relevant texts. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in out-domain zero-shot scenarios and outperformed existing methods in end-to-end evaluations. |
Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points . dense retrievers are limited by their inability to perform semantic matching for relevant passages that have low lexical overlap with the query. |
| Approach: | They propose a query expansion and reranking approach for improving passage retrieval with the application to open-domain question answering. |
| Outcome: | Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points when compared to a vanilla query expansion model and a dense retrieval model. |