Education
OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset
This dataset includes over 3.5 million documents with rich metadata, making it one of the most extensive collections of debate evidence.
Contextual Multinomial Logit Bandits with General Value Functions
Contextual multinomial logit (MNL) bandits capture many real-world assortment recommendation problems such as online retailing/advertising. However, prior work has only considered (generalized) linear value functions, which greatly limits its applicability. Motivated by this fact, in this work, we consider contextual MNL bandits with a general value function class that contains the ground truth, borrowing ideas from a recent trend of studies on contextual bandits. Specifically, we consider both the stochastic and the adversarial settings, and propose a suite of algorithms, each with different computation-regret trade-off. When applied to the linear case, our results not only are the first ones with no dependence on a certain problem-dependent constant that can be exponentially large, but also enjoy other advantages such as computational efficiency, dimension-free regret bounds, or the ability to handle completely adversarial contexts and rewards.
3bb42f6bb1b1ab6809afd6c90865b087-Paper-Datasets_and_Benchmarks_Track.pdf
QA, a multiple-choice trivia dataset that is parallel in English and Basque.
Linearly Decomposing and Recomposing Vision Transformers for Diverse-Scale Models Shuxia Lin
Vision Transformers (ViTs) are widely used in a variety of applications, while they usually have a fixed architecture that may not match the varying computational resources of different deployment environments.
Graph-based Uncertainty Metrics for Long-form Language Model Outputs
Recent advancements in Large Language Models (LLMs) have significantly improved text generation capabilities, but these systems are still known to hallucinate, and granular uncertainty estimation for long-form LLM generations remains challenging.
Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare
While recent advancements in large multimodal models (LMMs) have significantly improved their abilities in image quality assessment (IQA) relying on absolute quality rating, how to transfer reliable relative quality comparison outputs to continuous perceptual quality scores remains largely unexplored.
MatrixNet: Learning over symmetry groups using learned group representations
We also show that MatrixNet respects group relations allowing generalization to group elements of greater word length than in the training set.