Papers
DoubleDipper: Recycling Contexts for Efficient and Attributed In-Context Learning
Arie Cattan, Alon Jacovi, Alex Fabrikant et al.
Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusion
Markus Frohmann, Gabriel Meseguer-Brocal, Markus Schedl et al.
Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphs
Shuo Yang, Zheyu Zhang, Bardh Prenkaj et al.
Doubly-Bounded Queue for Constrained Online Learning: Keeping Pace with Dynamics of Both Loss and Constraint
Juncheng Wang, Bingjie Yan, Yituo Liu
Doubly Contrastive Learning for Source-Free Domain Adaptive Person Search
Yizhen Jia, Rong Quan, Yue Feng et al.
Doubly Optimal Policy Evaluation for Reinforcement Learning
Shuze Liu, Claire Chen, Shangtong Zhang
Doubly robust identification of treatment effects from multiple environments
Piersilvio De Bartolomeis, Julia Kostin, Javier Abad et al.
DOVE: A Large-Scale Multi-Dimensional Predictions Dataset Towards Meaningful LLM Evaluation
Eliya Habba, Ofir Arviv, Itay Itzhak et al.
Dovetail: A CPU/GPU Heterogeneous Speculative Decoding for LLM inference
Libo Zhang, Zhaoning Zhang, Xubaizhou et al.
Do Video Language Models really understand the video contexts?
Jeongwan Shin, Jinhyeong Lim, Hyeyoung Park
Do Vision & Language Decoders use Images and Text equally? How Self-consistent are their Explanations?
Letitia Parcalabescu, Anette Frank
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation
Qiyue Gao, Xinyu Pi, Kevin Liu et al.
Do Vision-Language Models Represent Space and How? Evaluating Spatial Frame of Reference under Ambiguities
Zheyuan Zhang, Fengyuan Hu, Jayjun Lee et al.
Do Visual Imaginations Improve Vision-and-Language Navigation Agents?
Akhil Perincherry, Jacob Krantz, Stefan Lee
Do Voters Get the Information They Want? Understanding Authentic Voter FAQs in the US and How to Improve for Informed Electoral Participation
Vipula Rawte, Deja N Scott, Gaurav Kumar et al.
Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild
Damien Teney, Liangze Jiang, Florin Gogianu et al.
Do We Know What LLMs Don’t Know? A Study of Consistency in Knowledge Probing
Raoyuan Zhao, Abdullatif Köksal, Ali Modarressi et al.
Do We Need Large VLMs for Spotting Soccer Actions?
Ritabrata Chakraborty, Rajatsubhra Chakraborty, Avijit Dasgupta et al.
Do We Need Large VLMs for Spotting Soccer Actions?
Ritabrata Chakraborty, Rajatsubhra Chakraborty, Avijit Dasgupta et al.
Do We Really Need All Those Dimensions? An Intrinsic Evaluation Framework for Compressed Embeddings
Nathan Inkiriwang, Necva Bölücü, Garth Tarr et al.
Do We Really Need Curated Malicious Data for Safety Alignment in Multi-modal Large Language Models?
Yanbo Wang, Jiyang Guan, Jian Liang et al.
Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad Prediction
Nils Constantin Hellwig, Jakob Fehle, Udo Kruschwitz et al.
Do WGANs succeed because they minimize the Wasserstein Distance? Lessons from Discrete Generators
Ariel Elnekave, Yair Weiss
Do What? Teaching Vision-Language-Action Models to Reject the Impossible
Wen-Han Hsieh, Elvis Hsieh, Dantong Niu et al.
Down the Cascades of Omethi: Hierarchical Automatic Scoring in Large-Scale Assessments
Fabian Zehner, Hyo Jeong Shin, Emily Kerzabi et al.