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[LG] Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition Z Xiong, Z Cai, J Cooper, A Ge... [University of Wisconsin-Madison] (2024) https://t.co/RVJUZ3mdhS https://t.co/AldnE5uGPD
🏷️:Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition 🔗:https://t.co/uwt5B7FTeG https://t.co/uFaAAVcIgb
LLMs are biased to do multiple things at once "Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition" https://t.co/6sYgrDRjo6 @zheyangxiong @jackcai1206 John Cooper @albert_ge_95 @vpapageorgiou_ Zack Sifakis @AngelikiGiannou Ziqian Lin… https://t.co/IqfFVrkwil
A recent 2024 study titled 'Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition' by Zheyang Xiong, Jack Cai, John Cooper, Albert Ge, Vasilis Papageorgiou, Zack Sifakis, Angeliki Giannou, and Ziqian Lin from the University of Wisconsin-Madison explores the capability of Large Language Models (LLMs) to learn multiple tasks simultaneously through in-context learning. The research highlights the potential of LLMs in reinforcement learning and artificial intelligence, suggesting that these models can handle multiple tasks at once without losing efficiency.
