Papers
Task Prototype-Based Knowledge Retrieval for Multi-Task Learning from Partially Annotated Data
Youngmin Oh, Hyung-Il Kim, Jung Uk Kim
Task-Specific Distance Correlation Matching for Few-Shot Action Recognition
Fei Long, Yao Zhang, Jiaming Lv et al.
Taxation Perspectives from Large Language Models: A Case Study on Additional Tax Penalties
Eunkyung Choi, Young Jin Suh, Siun Lee et al.
TaxonRL: Reinforcement Learning with Intermediate Rewards for Interpretable Fine-Grained Visual Reasoning
Maximilian von Klinski, Maximilian Schall
TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice
Gang Hu, Yating Chen, Haiyan Ding et al.
TaxReasoning: Benchmarking Knowledge-Intensive Mathematical Reasoning with Evolving Tax Laws
Nan Hu, Yike Wu, Jiaye Li et al.
TCoT: Trajectory Chain-of-Thoughts for Robotic Manipulation with Failure Recovery in Vision-Language-Action Model
Xiang Li, Ya-Li Li, Yuan Wang et al.
TDFlow: Agentic Workflows for Test Driven Development
Kevin Han, Siddharth Maddikayala, Tim Knappe et al.
TDSNNs: Competitive Topographic Deep Spiking Neural Networks for Visual Cortex Modeling
Deming Zhou, Yuetong Fang, Zhaorui Wang et al.
TDSS: Task Dynamic-Synergistic Skill Adaptation for Boosting Efficient and Scalable Multi-Task Learning in Dense Visual Prediction
Haiming Yao, Qiyu Chen, Wei Luo et al.
TEA-Bench: A Systematic Benchmarking of Tool-enhanced Emotional Support Dialogue Agent
Xingyu Sui, Yanyan Zhao, Yulin Hu et al.
Teach AI What It Doesn’t Know
Xuefeng Du
Teach a Reward Model to Correct Itself: Reward Guided Adversarial Failure Discovery for Robust Reward Modeling
Pankayaraj Pathmanathan, Furong Huang
Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies
Yuxuan Ye, Raul Santos-Rodriguez, Edwin Simpson
Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement Learning
Shuzheng Si, Haozhe Zhao, Cheng Gao et al.
Teaching LLMs Human-Like Editing of Inappropriate Argumentation via Reinforcement Learning
Timon Ziegenbein, Maja Stahl, Henning Wachsmuth
Teaching LLM to be Persuasive: Reward-Enhanced Policy Optimization for Alignment from Heterogeneous Rewards
Xia Zeng, Yihan Chen, Luhui Liu et al.
Teaching Modern NLP and LLMs at Kyiv School of Economics: A Practice-Oriented Course with Ukrainian Language Focus
Roman Kyslyi, Anton Bazdyrev
Teaching NLP in the AI Era: Experiences from the University of Latvia
Inguna Skadina, Guntis Barzdins, Uldis Bojārs et al.
Teaching Old Tokenizers New Words: Efficient Tokenizer Adaptation for Pretrained Models
Taido Purason, Pavel Chizhov, Ivan P. Yamshchikov et al.
Teaching Small Language Models to Learn Logic through Meta-Learning
Leonardo Bertolazzi, Manuel Vargas Guzmán, Raffaella Bernardi et al.
TeachMaster: Generative Teaching via Code
Yuheng Wang, Runde Yang, Lin Wu et al.
Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs
Wu Li, Yigeng Zhou, Zesheng Shi et al.