May 13, 2024, 4:41 a.m. | Meng Song, Xuezhi Wang, Tanay Biradar, Yao Qin, Manmohan Chandraker

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.06063v1 Announce Type: new
Abstract: Transformer-based methods have exhibited significant generalization ability when prompted with target-domain demonstrations or example solutions during inference. Although demonstrations, as a way of task specification, can capture rich information that may be hard to specify by language, it remains unclear what information is extracted from the demonstrations to help generalization. Moreover, assuming access to demonstrations of an unseen task is impractical or unreasonable in many real-world scenarios, especially in robotics applications. These questions motivate us …

abstract arxiv cs.lg domain example inference information language policy prompt solutions transformer type zero-shot

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Principal Research Engineer - Materials

@ GKN Aerospace | Westlake, TX, US