Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedback (2022.findings-naacl)
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Duy-Hung Nguyen, Nguyen Viet Dung Nghiem, Bao-Sinh Nguyen, Dung Tien Tien Le, Shahab Sabahi, Minh-Tien Nguyen, Hung Le
| Challenge: | a framework to train summarization models with preference feedback is proposed . human-in-the-loop (HITL) allows humans to actively participate in supervising AI systems . |
| Approach: | They propose a framework to train summarization models with preference feedback interactively. |
| Outcome: | The proposed framework improves ROUGE scores and sample-efficiency in active, few-shot and online settings. |
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