5 citations · 8 across the 17 of their papers we have counts for
17 papers
Bridge-RAG: An Abstract Bridge Tree Based Retrieval Augmented Generation Algorithm
Zihang Li, Wenjun Liu, Yikun Zong +7
As an important paradigm for enhancing the generation quality of Large Language Models (LLMs), retrieval-augmented generation (RAG) faces the two challenges regarding retrieval acc…
Evaluating and Calibrating LLM Confidence on Questions with Multiple Correct Answers
Yuhan Wang, Shiyu Ni, Zhikai Ding +3
Confidence calibration is essential for making large language models (LLMs) reliable, yet existing training-free methods have been primarily studied under single-answer question an…
EntroCoT: Enhancing Chain-of-Thought via Adaptive Entropy-Guided Segmentation
Zihang Li, Yuhang Wang, Yikun Zong +6
Chain-of-Thought (CoT) prompting has significantly enhanced the mathematical reasoning capabilities of Large Language Models. We find existing fine-tuning datasets frequently suffe…
Reasoning Shapes Alignment: Investigating Cultural Alignment in Large Reasoning Models with Cultural Norms
Yuhang Wang, Yanxu Zhu, Jitao Sang
The advanced reasoning capabilities of Large Reasoning Models enable them to thoroughly understand and apply safety policies through deliberate thought processes, thereby improving…
Self-Guided Adaptive Safety Alignment: Synthesizing and Internalizing Guidelines in Reasoning Models
Yuhang Wang, Yanxu Zhu, Jiaming Zhang +2
Explicit safety policies can improve reasoning-model safety, but their effective coverage may lag behind evolving jailbreak strategies. We study whether a reasoning model can synth…
Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native Agentic AI
Jitao Sang, Jinlin Xiao, Jiarun Han +5
The rapid evolution of agentic AI marks a new phase in artificial intelligence, where Large Language Models (LLMs) no longer merely respond but act, reason, and adapt. This survey…
XFacta: Contemporary, Real-World Dataset and Evaluation for Multimodal Misinformation Detection with Multimodal LLMs
Yuzhuo Xiao, Zeyu Han, Yuhan Wang +1
The rapid spread of multimodal misinformation on social media calls for more effective and robust detection methods. Recent advances leveraging multimodal large language models (ML…
Thinking Short and Right Over Thinking Long: Serving LLM Reasoning Efficiently and Accurately
Yuhang Wang, Youhe Jiang, Bin Cui +1
Recent advances in test-time scaling suggest that Large Language Models (LLMs) can gain better capabilities by generating Chain-of-Thought reasoning (analogous to human thinking) t…
Neuro-Conceptual Artificial Intelligence: Integrating OPM with Deep Learning to Enhance Question Answering Quality
Xin Kang, Veronika Shteingardt, Yuhan Wang +1
Knowledge representation and reasoning are critical challenges in Artificial Intelligence (AI), particularly in integrating neural and symbolic approaches to achieve explainable an…
OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning
Yuxiang Zhang, Yuqi Yang, Jiangming Shu +3
OpenAI's recent introduction of Reinforcement Fine-Tuning (RFT) showcases the potential of reasoning foundation model and offers a new paradigm for fine-tuning beyond simple patter…
Enhancing Cluster Resilience: LLM-agent Based Autonomous Intelligent Cluster Diagnosis System and Evaluation Framework
Honghao Shi, Longkai Cheng, Wenli Wu +7
Recent advancements in Large Language Models (LLMs) and related technologies such as Retrieval-Augmented Generation (RAG) and Diagram of Thought (DoT) have enabled the creation of…
Don't Command, Cultivate: An Exploratory Study of System-2 Alignment
Yuhang Wang, Yuxiang Zhang, Yanxu Zhu +2
The o1 system card identifies the o1 models as the most robust within OpenAI, with their defining characteristic being the progression from rapid, intuitive thinking to slower, mor…
KG-FPQ: Evaluating Factuality Hallucination in LLMs with Knowledge Graph-based False Premise Questions
Yanxu Zhu, Jinlin Xiao, Yuhang Wang +1
Recent studies have demonstrated that large language models (LLMs) are susceptible to being misled by false premise questions (FPQs), leading to errors in factual knowledge, know a…
Development of a Reliable and Accessible Caregiving Language Model (CaLM)
Bambang Parmanto, Bayu Aryoyudanta, Wilbert Soekinto +5
Unlike professional caregivers, family caregivers often assume this role without formal preparation or training. Because of this, there is an urgent need to enhance the capacity of…
Improving Weak-to-Strong Generalization with Scalable Oversight and Ensemble Learning
Jitao Sang, Yuhang Wang, Jing Zhang +5
This paper presents a follow-up study to OpenAI's recent superalignment work on Weak-to-Strong Generalization (W2SG). Superalignment focuses on ensuring that high-level AI systems…
CDEval: A Benchmark for Measuring the Cultural Dimensions of Large Language Models
Yuhang Wang, Yanxu Zhu, Chao Kong +4
As the scaling of Large Language Models (LLMs) has dramatically enhanced their capabilities, there has been a growing focus on the alignment problem to ensure their responsible and…
AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation
Junyang Wang, Yuhang Wang, Guohai Xu +8
Despite making significant progress in multi-modal tasks, current Multi-modal Large Language Models (MLLMs) encounter the significant challenge of hallucinations, which may lead to…