We present LLM-Blender, an ensembling framework designed to attain consistently superior
performance by leveraging the diverse strengths of multiple open-source large language …

We consider data in the form of pairwise comparisons of n items, with the goal of identifying
the top k items for some value of k< n, or alternatively, recovering a ranking of all the items …

This paper considers the problem of learning an embedding of data based on similarity
triplets of the form “A is more similar to B than to C”. This learning setting is of relevance to …

This paper is concerned with the problem of top-K ranking from pairwise comparisons. Given
a collection of n items and a few pairwise comparisons across them, one wishes to identify …

The ranking of n objects based on pairwise comparisons is a core machine learning
problem, arising in recommender systems, ad placement, player ranking, biological …

Recently, learning with soft labels has been shown to achieve better performance than
learning with hard labels in terms of model generalization, calibration, and robustness …

We introduce a new representation learning approach for domain adaptation, in which data
at training and test time come from similar but different distributions. Our approach is directly …

Computational representation of everyday emotional states is a challenging task and,
arguably, one of the most fundamental for affective computing. Standard practice in emotion …

This paper explores the preference-based top-K rank aggregation problem. Suppose that a
collection of items is repeatedly compared in pairs, and one wishes to recover a consistent …

Social choice becomes easier on restricted preference domains such as single-peaked,
single-crossing, and Euclidean preferences. Many impossibility theorems disappear, the …