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AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators
Research article (Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track), 2024) · cited 75× · AI/ML
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APA4ort.xyz Knowledge Graph. (2026). AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators. Retrieved May 24, 2026, from https://4ort.xyz/entity/annollm-making-large-language-models-to-be-better-crowdsourced-annotators
MLA“AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/annollm-making-large-language-models-to-be-better-crowdsourced-annotators.
BibTeX@misc{4ortxyz_annollm-making-large-language-models-to-be-better-crowdsourced-annotators_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators}}, year = {2026}, url = {https://4ort.xyz/entity/annollm-making-large-language-models-to-be-better-crowdsourced-annotators}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators — https://4ort.xyz/entity/annollm-making-large-language-models-to-be-better-crowdsourced-annotators (retrieved 2026-05-24)