Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Heuss, Maria"'
Retrieving relevant context is a common approach to reduce hallucinations and enhance answer reliability. Explicitly citing source documents allows users to verify generated responses and increases trust. Prior work largely evaluates citation correct
Externí odkaz:
http://arxiv.org/abs/2412.18004
Feature attributions are a commonly used explanation type, when we want to posthoc explain the prediction of a trained model. Yet, they are not very well explored in IR. Importantly, feature attribution has rarely been rigorously defined, beyond attr
Externí odkaz:
http://arxiv.org/abs/2403.16085
Publikováno v:
CIKM 2023: 32nd ACM International Conference on Information and Knowledge Management
Societal biases that are contained in retrieved documents have received increased interest. Such biases, which are often prevalent in the training data and learned by the model, can cause societal harms, by misrepresenting certain groups, and by enfo
Externí odkaz:
http://arxiv.org/abs/2309.09833
Fairness of exposure is a commonly used notion of fairness for ranking systems. It is based on the idea that all items or item groups should get exposure proportional to the merit of the item or the collective merit of the items in the group. Often,
Externí odkaz:
http://arxiv.org/abs/2205.12901
Traditional ranking systems are expected to sort items in the order of their relevance and thereby maximize their utility. In fair ranking, utility is complemented with fairness as an optimization goal. Recent work on fair ranking focuses on developi
Externí odkaz:
http://arxiv.org/abs/2112.11251
Autor:
Anand, Avishek1 (AUTHOR) avishek.anand@tudelft.nl, Pera, Maria Soledad1 (AUTHOR) m.s.pera@tudelft.nl, Heuss, Maria2 (AUTHOR) m.c.heuss@uva.nl, V, Venktesh3 (AUTHOR) v.viswanathan-1@tudelft.nl, Corsi, Matteo3 (AUTHOR) m.corsi@tudelft.nl
Publikováno v:
SIGIR Forum. Dec2023, Vol. 57 Issue 2, p1-5. 5p.
