Google Research AI blog

APRIL 21, 2023

It usually involves a cross-functional team of ML practitioners who fine-tune the models, evaluate robustness, characterize strengths and weaknesses, inspect performance in the end-use context, and develop the applications. Sign up to be notified when Visual Blocks for ML is publicly available.

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Interaction Learning Tools Evaluation 121

Google Research AI blog

APRIL 30, 2023

Liu SMART: Sentences as Basic Units for Text Evaluation Reinald Kim Amplayo , Peter J. Zhao , Ji Ma , Yi Luan , Jianmo Ni , Jing Lu , Anton Bakalov , Kelvin Guu , Keith B. Dillon Calibrating Sequence Likelihood Improves Conditional Language Generation Yao Zhao , Misha Khalman , Rishabh Joshi , Shashi Narayan , Mohammad Saleh , Peter J.

Google 105

Google Language Jing Model 105

Stanford AI Lab Blog

DECEMBER 6, 2021

Powers, Yianni Laloudakis, Sidhika Balachandar, Bowen Jing, Brandon Anderson, Stephan Eismann, Risi Kondor, Russ B. Townshend, Martin Vögele, Patricia Suriana, Alexander Derry, Alexander S. Altman, Ron O.

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Contact Learning Theory Authoring Offline 40

Stanford AI Lab Blog

FEBRUARY 1, 2022

These models perform well when evaluated by crowdworkers in carefully-controlled settings–typically written conversations with certain topical or length constraints. In this work, we conduct a large-scale quantitative evaluation of response strategies against offensive users in-the-wild.

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Alexa Experiment Team Conversation 40