Results 271 to 280 of about 5,543,464 (336)

Interpretation of users’ feedback via swarmed particles for content-based image retrieval

Information Sciences, 2017
Jianmin Jiang, Ying Ding, Chunna Tian
exaly   +2 more sources

Training language models to follow instructions with human feedback

Neural Information Processing Systems, 2022
Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user.
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, S. Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke E. Miller, M. Simens, Amanda Askell, Peter Welinder, P. Christiano, Jan Leike, Ryan J. Lowe   +19 more
semanticscholar   +1 more source

How do users’ feedback influence creators’ contributions: an empirical study of an online music community

Behavior and Information Technology, 2022
This paper examines creators’ contributions and the incentive effects of users’ feedback, including upvoting, sharing, commenting, following, viewing comments, and clicking on the creators’ homepage.
Yao Wang, A. Majeed
semanticscholar   +1 more source

A Semantic-Based Framework for Analyzing App Users' Feedback

IEEE International Conference on Software Analysis, Evolution, and Reengineering, 2020
The competitive market of mobile apps requires app developers to consider the users' feedback frequently. This feedback, when comes from different resources, e.g.
Aman Yadav, Rishab Sharma, F. H. Fard
semanticscholar   +1 more source

User Feedback in Mobile Development

Proceedings of the 2nd International Workshop on Mobile Development Lifecycle, 2014
Developers need to obtain feedback early to build applications that fit to the users needs. In this paper we show how the combination of two approaches enables developers to continuously improve usability and user experience of mobile applications. The Tornado model is a light-weight scenario-based approach for producing executable prototypes.
Stephan Krusche, Bernd Brügge
openaire   +1 more source

Continuously Learning from User Feedback

2022
Machine Learning has been evolving rapidly over the past years, with new algorithms and approaches being devised to solve the challenges that the new properties of data pose. Specifically, algorithms must now learn continuously and in real time, from very large and possibly distributed sets of data.
Davide Carneiro, Miguel Sousa, Guilherme Palumbo, Miguel Guimarães, Mariana Carvalho, Paulo Novais   +5 more
openaire   +2 more sources