Papers with zero-shot way
Towards Open-Domain Topic Classification (2022.naacl-demo)
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| Challenge: | Existing supervised classification models are insensitive to class names, but are no longer effective in open-domain tasks where the taxonomy is unbounded. |
| Approach: | They propose a topic classification system that accepts user-defined taxonomy in real time . they train a pretrained language model on a new Wikipedia dataset and train it on Wikipedia . |
| Outcome: | The proposed system improves over existing zero-shot models and performs competitively with weakly-supervised models trained on in-domain data. |
Smaller Language Models are Better Zero-shot Machine-Generated Text Detectors (2024.eacl-short)
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| Challenge: | Using large language models to detect machine generated text is difficult for humans to distinguish between human-written and machine-generated text. |
| Approach: | They propose to use one language model to detect machine-generated text produced by another language model in a zero-shot way. |
| Outcome: | The proposed methods can detect machine-generated text without additional training/data. |
Preventing Author Profiling through Zero-Shot Multilingual Back-Translation (2021.emnlp-main)
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| Challenge: | Documents as short as a single sentence may reveal sensitive information about authors . style transfer is effective but a number of current methods cause a drop in down-stream utility . |
| Approach: | They propose a method to remove sensitive information from documents by multilingual back-translation using off-the-shelf translation models. |
| Outcome: | The proposed method lowers adversarial gender and race prediction by 22% while retaining 95% of original utility on downstream tasks. |
Query-driven Relevant Paragraph Extraction from Legal Judgments (2024.lrec-main)
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| Challenge: | Legal professionals struggle with navigating lengthy legal judgements to pinpoint information that directly addresses their queries. |
| Approach: | They construct a specialized dataset to extract relevant paragraphs from legal judgements based on query . they assess the performance of current retrieval models in a zero-shot way . |
| Outcome: | The proposed model outperforms the current retrieval models in a zero-shot way and fine-tunes them using various models. |