Papers
11,015 papers found
Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning
Haque Ishfaq, Guangyuan Wang, Sami Nur Islam et al.
Language Agents Meet Causality -- Bridging LLMs and Causal World Models
John Gkountouras, Matthias Lindemann, Phillip Lippe et al.
Language-Assisted Feature Transformation for Anomaly Detection
EungGu Yun, Heonjin Ha, Yeongwoo Nam et al.
Language Guided Skill Discovery
Seungeun Rho, Laura Smith, Tianyu Li et al.
Language-Image Models with 3D Understanding
Jang Hyun Cho, Boris Ivanovic, Yulong Cao et al.
Language Imbalance Driven Rewarding for Multilingual Self-improving
Wen Yang, Junhong Wu, Chen Wang et al.
Language Model Alignment in Multilingual Trolley Problems
Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti et al.
Language Models are Advanced Anonymizers
Robin Staab, Mark Vero, Mislav Balunovic et al.
Language Models Are Implicitly Continuous
Samuele Marro, Davide Evangelista, X. Angelo Huang et al.
Language Models Learn to Mislead Humans via RLHF
Jiaxin Wen, Ruiqi Zhong, Akbir Khan et al.
Language Models Need Inductive Biases to Count Inductively
Yingshan Chang, Yonatan Bisk
Language models scale reliably with over-training and on downstream tasks
Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar et al.
Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice
Jian-Qiao Zhu, Haijiang Yan, Thomas L. Griffiths
Language Representations Can be What Recommenders Need: Findings and Potentials
Leheng Sheng, An Zhang, Yi Zhang et al.
LANTERN: Accelerating Visual Autoregressive Models with Relaxed Speculative Decoding
Doohyuk Jang, Sihwan Park, June Yong Yang et al.
Laplace Sample Information: Data Informativeness Through a Bayesian Lens
Johannes Kaiser, Kristian Schwethelm, Daniel Rueckert et al.
Large Convolutional Model Tuning via Filter Subspace
Wei Chen, Zichen Miao, Qiang Qiu
Large Language Models are Interpretable Learners
Ruochen Wang, Si Si, Felix Yu et al.
Large Language Models Assume People are More Rational than We Really are
Ryan Liu, Jiayi Geng, Joshua Peterson et al.
Large Language Models can Become Strong Self-Detoxifiers
Ching-Yun Ko, Pin-Yu Chen, Payel Das et al.
Large Language Models Meet Symbolic Provers for Logical Reasoning Evaluation
Chengwen Qi, Ren Ma, Bowen Li et al.
Large Language Models Often Say One Thing and Do Another
Ruoxi Xu, Hongyu Lin, Xianpei Han et al.
Large-scale and Fine-grained Vision-language Pre-training for Enhanced CT Image Understanding
Zhongyi Shui, Jianpeng Zhang, Weiwei Cao et al.
Large Scale Knowledge Washing
Yu Wang, Ruihan Wu, Zexue He et al.
Large (Vision) Language Models are Unsupervised In-Context Learners
Artyom Gadetsky, Andrei Atanov, Yulun Jiang et al.