1 citations · 2 across the 18 of their papers we have counts for
18 papers
CrowdAgent: Multi-Agent Managed Multi-Source Annotation System
Maosheng Qin, Renyu Zhu, Mingxuan Xia +8
High-quality annotated data is a cornerstone of modern Natural Language Processing (NLP). While recent methods begin to leverage diverse annotation sources-including Large Language…
Empowering Economic Simulation for Massively Multiplayer Online Games through Generative Agent-Based Modeling
Bihan Xu, Shiwei Zhao, Runze Wu +10
Within the domain of Massively Multiplayer Online (MMO) economy research, Agent-Based Modeling (ABM) has emerged as a robust tool for analyzing game economics, evolving from rule-b…
Fast-DataShapley: Neural Modeling for Training Data Valuation
Haifeng Sun, Yu Xiong, Runze Wu +4
The value and copyright of training data are crucial in the artificial intelligence industry. Service platforms should protect data providers' legitimate rights and fairly reward t…
MVP-Shapley: Feature-based Modeling for Evaluating the Most Valuable Player in Basketball
Haifeng Sun, Yu Xiong, Runze Wu +5
The burgeoning growth of the esports and multiplayer online gaming community has highlighted the critical importance of evaluating the Most Valuable Player (MVP). The establishment…
The Effects of Data Augmentation on Confidence Estimation for LLMs
Rui Wang, Renyu Zhu, Minmin Lin +4
Confidence estimation is crucial for reflecting the reliability of large language models (LLMs), particularly in the widely used closed-source models. Utilizing data augmentation f…
Digital Player: Evaluating Large Language Models based Human-like Agent in Games
Jiawei Wang, Kai Wang, Shaojie Lin +11
With the rapid advancement of Large Language Models (LLMs), LLM-based autonomous agents have shown the potential to function as digital employees, such as digital analysts, teacher…
Rank Aggregation in Crowdsourcing for Listwise Annotations
Wenshui Luo, Haoyu Liu, Yongliang Ding +7
Rank aggregation through crowdsourcing has recently gained significant attention, particularly in the context of listwise ranking annotations. However, existing methods primarily f…
Reinforcement Learning From Imperfect Corrective Actions And Proxy Rewards
Zhaohui Jiang, Xuening Feng, Paul Weng +6
In practice, reinforcement learning (RL) agents are often trained with a possibly imperfect proxy reward function, which may lead to a human-agent alignment issue (i.e., the learne…
StyleTalk++: A Unified Framework for Controlling the Speaking Styles of Talking Heads
Suzhen Wang, Yifeng Ma, Yu Ding +5
Individuals have unique facial expression and head pose styles that reflect their personalized speaking styles. Existing one-shot talking head methods cannot capture such personali…
Bayesian Design Principles for Offline-to-Online Reinforcement Learning
Hao Hu, Yiqin Yang, Jianing Ye +7
Offline reinforcement learning (RL) is crucial for real-world applications where exploration can be costly or unsafe. However, offline learned policies are often suboptimal, and fu…
vMFER: Von Mises-Fisher Experience Resampling Based on Uncertainty of Gradient Directions for Policy Improvement
Yiwen Zhu, Jinyi Liu, Wenya Wei +7
Reinforcement Learning (RL) is a widely employed technique in decision-making problems, encompassing two fundamental operations -- policy evaluation and policy improvement. Enhanci…
A Dataset for the Validation of Truth Inference Algorithms Suitable for Online Deployment
Fei Wang, Haoyu Liu, Haoyang Bi +9
For the purpose of efficient and cost-effective large-scale data labeling, crowdsourcing is increasingly being utilized. To guarantee the quality of data labeling, multiple annotat…
ICE: Interactive 3D Game Character Editing via Dialogue
Haoqian Wu, Minda Zhao, Zhipeng Hu +5
ost recent popular Role-Playing Games (RPGs) allow players to create in-game characters with hundreds of adjustable parameters, including bone positions and various makeup options.…
XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques
Yu Xiong, Zhipeng Hu, Ye Huang +9
Reinforcement Learning (RL) has demonstrated substantial potential across diverse fields, yet understanding its decision-making process, especially in real-world scenarios where ra…
Towards a Simultaneous and Granular Identity-Expression Control in Personalized Face Generation
Renshuai Liu, Bowen Ma, Wei Zhang +5
In human-centric content generation, the pre-trained text-to-image models struggle to produce user-wanted portrait images, which retain the identity of individuals while exhibiting…
Towards Long-term Annotators: A Supervised Label Aggregation Baseline
Haoyu Liu, Fei Wang, Minmin Lin +6
Relying on crowdsourced workers, data crowdsourcing platforms are able to efficiently provide vast amounts of labeled data. Due to the variability in the annotation quality of crow…
A New Baseline Assumption of Integated Gradients Based on Shaply value
Shuyang Liu, Zixuan Chen, Ge Shi +6
Efforts to decode deep neural networks (DNNs) often involve mapping their predictions back to the input features. Among these methods, Integrated Gradients (IG) has emerged as a si…
AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model
Zibin Dong, Yifu Yuan, Jianye Hao +7
Aligning agent behaviors with diverse human preferences remains a challenging problem in reinforcement learning (RL), owing to the inherent abstractness and mutability of human pre…