Google Research AI blog
APRIL 21, 2023
Researchers optimize the model with quantitative metrics on a fixed set of data, but real-world performance requires human reviewers to evaluate in the application context. Participants could not quickly and interactively alter the input data or tune the model. Acknowledgements This work is a collaboration across multiple teams at Google.
Google Research AI blog
JUNE 6, 2023
We measured the performance of the fine-tuned model with the token accuracy metric, i.e., the percentage of tokens in a batch that were correctly predicted by the model. We used 1276 (80%) examples from the VC1.5K dataset for fine-tuning the large language model and the remaining 319 (20%) examples as test data.
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Beth's Blog: How Nonprofits Can Use Social Media
AUGUST 10, 2007
As both a lesson and as a metric, failure is potentially productive at every level of socialmarkets - from the repeated return of one homeless person to shelter, to the repeated attempts to attach a value like SROI to such a story. The "Failure is an option" particularly caught my eye.
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.
Stanford AI Lab Blog
FEBRUARY 1, 2022
While there is a large body of prior work attempting to address this issue, most prior approaches use qualitative metrics based on surveys conducted in lab settings. We believe that these metrics measure the effectiveness of a response strategy more directly than user ratings as done in Cohn et al.
Google Research AI blog
NOVEMBER 28, 2022
Chi The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning Yunhao Tang, Mark Rowland, Rémi Munos, Bernardo Ávila Pires, Will Dabney, Marc G.
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