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Towards Effective GenAI Multi-Agent Collaboration: Design and Evaluation for Enterprise Applications
AI agents powered by large language models (LLMs) have shown strong capabilities in problem solving. Through combining many intelligent agents, multi-agent collaboration has emerged as a promising approach to tackle complex, multi-faceted problems th
Externí odkaz:
http://arxiv.org/abs/2412.05449
Autor:
Barham, Samuel, Weller, Orion, Yuan, Michelle, Murray, Kenton, Yarmohammadi, Mahsa, Jiang, Zhengping, Vashishtha, Siddharth, Martin, Alexander, Liu, Anqi, White, Aaron Steven, Boyd-Graber, Jordan, Van Durme, Benjamin
To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages, along with their 71 million referenced source materials. We process t
Externí odkaz:
http://arxiv.org/abs/2307.07049
Autor:
Mondal, Ishani, Yuan, Michelle, N, Anandhavelu, Garimella, Aparna, Ferraro, Francis, Blair-Stanek, Andrew, Van Durme, Benjamin, Boyd-Graber, Jordan
Learning template based information extraction from documents is a crucial yet difficult task. Prior template-based IE approaches assume foreknowledge of the domain templates; however, real-world IE do not have pre-defined schemas and it is a figure-
Externí odkaz:
http://arxiv.org/abs/2305.14659
Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active learning is well-defined for cl
Externí odkaz:
http://arxiv.org/abs/2104.07611
Active learning strives to reduce annotation costs by choosing the most critical examples to label. Typically, the active learning strategy is contingent on the classification model. For instance, uncertainty sampling depends on poorly calibrated mod
Externí odkaz:
http://arxiv.org/abs/2010.09535
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: Ph. D. in Linguistics, Massachusetts Institute of Technology, Department of Linguistics and
Thesis: Ph. D. in Linguistics, Massachusetts Institute of Technology, Department of Linguistics and
Externí odkaz:
https://hdl.handle.net/1721.1/122054
Cross-lingual word embeddings transfer knowledge between languages: models trained on high-resource languages can predict in low-resource languages. We introduce CLIME, an interactive system to quickly refine cross-lingual word embeddings for a given
Externí odkaz:
http://arxiv.org/abs/1911.03070
Akademický článek
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Autor:
Yuan, Michelle
Publikováno v:
Natural Language & Linguistic Theory, 2020 Aug 01. 38(3), 937-985.
Externí odkaz:
https://www.jstor.org/stable/48741768
Akademický článek
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