Papers with automatically search
Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts (2022.acl-long)
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| Challenge: | Existing methods to mitigate human-like biases in pretrained language models are based on external corpora and require a distribution alignment loss to mitigate them. |
| Approach: | They propose an automatic method to mitigate biases in pretrained language models by searching for biased prompts such that cloze-style completions are the most different with respect to different demographic groups. |
| Outcome: | The proposed method reduces biases in pretrained language models, including gender and racial bias, and improves fairness of the models. |
Language Models can Categorize System Inputs for Performance Analysis (2025.naacl-long)
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| Challenge: | Existing evaluations only provide a single numerical score for broad categories. |
| Approach: | They propose to automatically search for finer-grained categories based on inputs where a system performs well or poorly and describe them in natural language. |
| Outcome: | The proposed model compares LLaMA 3-70B and Claude 3 Opus with similar Elo ratings on Chatbot Arena. |
Auto-hMDS: Automatic Construction of a Large Heterogeneous Multilingual Multi-Document Summarization Corpus (L18-1)
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| Challenge: | Existing datasets for automatic text summarization are small and focused on newswires. |
| Approach: | They propose to automatically generate a large multilingual multi-document summarization corpus using Wikipedia articles as summaries and to automatically search for appropriate source documents. |
| Outcome: | The proposed corpus contains 7,316 topics in English and German with different summary lengths and number of source documents. |
GPS: Genetic Prompt Search for Efficient Few-Shot Learning (2022.emnlp-main)
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| Challenge: | Pretrained language models are often finetuned for downstream tasks, which has been shown to improve performance over non-pretrained models. |
| Approach: | They propose a genetic algorithm to automatically search for the best prompt for few-shot learning with pretrained language models by gradient-free algorithm. |
| Outcome: | Experiments on diverse datasets show that the proposed method outperforms manual prompts by 2.6 points. |
Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification (2021.emnlp-main)
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| Challenge: | Data augmentation aims to alleviate the overfitting issue in low-resource or class-imbalanced situations. |
| Approach: | They propose a framework called Text AutoAugment to enhance training samples . they use a Bayesian optimization algorithm to search for the best policy . |
| Outcome: | The proposed framework outperforms baseline methods on six benchmark datasets. |