| Challenge: | Existing supervised learning methods in natural language processing require large amounts of data. |
| Approach: | They propose an active learning loop that takes LLMs as annotators and puts them into an active loop to determine what to annotate efficiently. |
| Outcome: | The proposed model outperforms existing models with few-shot performance in two NLP tasks. |
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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley
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Large Language Models for Data Annotation and Synthesis: A Survey (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have advanced the field of NLP significantly, but deploying them for downstream applications is still challenging due to cost, responsiveness, control, or concerns around privacy and security. |
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| Challenge: | Active learning strategies struggle with a ‘cold-start’ problem, needing substantial initial data to be effective. |
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| Challenge: | a longstanding strategy to reduce annotation costs is active learning . data annotation is expected to remain important and active learning to stay relevant . |
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Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models (2024.naacl-short)
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Tomáš Horych, Christoph Mandl, Terry Ruas, Andre Greiner-Petter, Bela Gipp, Akiko Aizawa, Timo Spinde
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FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models (2023.emnlp-main)
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| Challenge: | Modern machine learning models require a huge collection of precisely labeled data, which can be labor-intensive and time-consuming. |
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Large Language Models for Generative Recommendation: A Survey and Visionary Discussions (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing but are not fully able to leverage the generative power of LLM. |
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RED-CT: A Systems Design Methodology for Using LLM-labeled Data to Train and Deploy Edge Linguistic Classifiers (2025.coling-industry)
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| Challenge: | Large language models have improved our ability to rapidly analyze and classify unstructured natural language data. |
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