11 citations · 26 across the 8 of their papers we have counts for
8 papers
OpenHalDet: A Unified Benchmark for Hallucination Detection across Diverse Generation Scenarios
Xinyi Li, Zhen Fang, Yongxin Deng +12
Hallucination detection is essential for the reliable deployment of large language models (LLMs). However, existing evaluations face two core challenges: inconsistent inference con…
Beyond In-Domain Detection: SpikeScore for Cross-Domain Hallucination Detection
Yongxin Deng, Zhen Fang, Sharon Li +1
Hallucination detection is critical for deploying large language models (LLMs) in real-world applications. Existing hallucination detection methods achieve strong performance when…
Adaptive Learning on User Segmentation: Universal to Specific Representation via Bipartite Neural Interaction
Xiaoyu Tan, Yongxin Deng, Chao Qu +4
Recently, models for user representation learning have been widely applied in click-through-rate (CTR) and conversion-rate (CVR) prediction. Usually, the model learns a universal u…
CogniDual Framework: Self-Training Large Language Models within a Dual-System Theoretical Framework for Improving Cognitive Tasks
Yongxin Deng, Xihe Qiu, Xiaoyu Tan +5
Cognitive psychology investigates perception, attention, memory, language, problem-solving, decision-making, and reasoning. Kahneman's dual-system theory elucidates the human decis…
FedSlate:A Federated Deep Reinforcement Learning Recommender System
Yongxin Deng, Xihe Qiu, Xiaoyu Tan +1
Reinforcement learning methods have been used to optimize long-term user engagement in recommendation systems. However, existing reinforcement learning-based recommendation systems…
Reward Guidance for Reinforcement Learning Tasks Based on Large Language Models: The LMGT Framework
Yongxin Deng, Xihe Qiu, Jue Chen +1
The inherent uncertainty in the environmental transition model of Reinforcement Learning (RL) necessitates a delicate balance between exploration and exploitation. This balance is…
BTBR: A Bayesian-Theory-Driven Probabilistic-Fuzzy Framework for Implicit Bias Removal in Large Language Models
Yongxin Deng, Xihe Qiu, Xiaoyu Tan +8
Large language models (LLMs) may encode biased associations from heterogeneous training corpora that are not immediately visible under ordinary prompting, but can surface when the…
Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Bootstrapped Prolog-based Chain-of-Thought
Xiaoyu Tan, Yongxin Deng, Xihe Qiu +7
Large language models have demonstrated remarkable capabilities as general-purpose assistants, excelling in a wide range of reasoning tasks and supporting various aspects of daily…