9 citations · 16 across the 10 of their papers we have counts for
6 papers · 1 filter
Taxonomy of User Needs and Actions
Renee Shelby, Fernando Diaz, Vinodkumar Prabhakaran
The growing ubiquity of conversational AI highlights the need for frameworks that capture not only users' instrumental goals but also the situated, adaptive, and social practices t…
RankList -- A Listwise Preference Learning Framework for Predicting Subjective Preferences
Abinay Reddy Naini, Fernando Diaz, Carlos Busso
Preference learning has gained significant attention in tasks involving subjective human judgments, such as \emph{speech emotion recognition} (SER) and image aesthetic assessment.…
MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers
Jushaan Singh Kalra, Xinran Zhao, To Eun Kim +3
Retrieval-augmented Generation (RAG) is powerful, but its effectiveness hinges on which retrievers we use and how. Different retrievers offer distinct, often complementary signals:…
Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor
Alexandra Olteanu, Su Lin Blodgett, Agathe Balayn +7
In AI research and practice, rigor remains largely understood in terms of methodological rigor -- such as whether mathematical, statistical, or computational methods are correctly…
LTRR: Learning To Rank Retrievers for LLMs
To Eun Kim, Fernando Diaz
Retrieval-Augmented Generation (RAG) systems typically rely on a single fixed retriever, despite growing evidence that no single retriever performs optimally across all query types…
Contextual Metric Meta-Evaluation by Measuring Local Metric Accuracy
Athiya Deviyani, Fernando Diaz
Meta-evaluation of automatic evaluation metrics -- assessing evaluation metrics themselves -- is crucial for accurately benchmarking natural language processing systems and has imp…