10 citations · 15 across the 15 of their papers we have counts for
15 papers
Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning
Xinyan Guan, Jiali Zeng, Chunlei Xin +5
Large language models generate computationally expensive yet semantically void reasoning on beyond-capability tasks, creating risks where plausible-sounding but incorrect derivatio…
Your Teacher Can't Help You Here: Combating Supervision Fidelity Decay in On-Policy Distillation
Yanjiang Liu, Jie Lou, Xinyan Guan +7
On-policy distillation transfers reasoning capabilities by training a student model on its own generated trajectories using token-level feedback from a teacher. However, we identif…
Reconstructing 12-Lead ECG from 3-Lead ECG using Variational Autoencoder to Improve Cardiac Disease Detection of Wearable ECG Devices
Xinyan Guan, Yongfan Lai, Jiarui Jin +6
Twelve-lead electrocardiograms (ECGs) are the clinical gold standard for cardiac diagnosis, providing comprehensive spatial coverage of the heart necessary to detect conditions suc…
When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models
Yingzhi Mao, Chunkang Zhang, Junxiang Wang +6
Large Reasoning Models (LRMs) achieve strong performance on complex multi-step reasoning, yet they still exhibit severe safety failures such as harmful content generation. Existing…
ECGTwin: Personalized ECG Generation Using Controllable Diffusion Model
Yongfan Lai, Bo Liu, Xinyan Guan +3
Personalized electrocardiogram (ECG) generation is to simulate a patient's ECG digital twins tailored to specific conditions. It has the potential to transform traditional healthca…
Dissecting Long-Chain-of-Thought Reasoning Models: An Empirical Study
Yongyu Mu, Jiali Zeng, Bei Li +5
Despite recent progress in training long-chain-of-thought reasoning models via scaling reinforcement learning (RL), its underlying training dynamics remain poorly understood, and s…
ConsistentChat: Building Skeleton-Guided Consistent Multi-Turn Dialogues for Large Language Models from Scratch
Jiawei Chen, Xinyan Guan, Qianhao Yuan +7
Current instruction data synthesis methods primarily focus on single-turn instructions and often neglect cross-turn coherence, resulting in context drift and reduced task completio…
PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides
Hao Zheng, Xinyan Guan, Hao Kong +7
Automatically generating presentations from documents is a challenging task that requires accommodating content quality, visual appeal, and structural coherence. Existing methods p…
DeepRAG: Thinking to Retrieve Step by Step for Large Language Models
Xinyan Guan, Jiali Zeng, Fandong Meng +6
Large Language Models (LLMs) have shown remarkable reasoning capabilities, while their practical applications are limited by severe factual hallucinations due to limitations in the…
Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering
Xinyan Guan, Yanjiang Liu, Xinyu Lu +8
The evolution of machine learning has increasingly prioritized the development of powerful models and more scalable supervision signals. However, the emergence of foundation models…
REInstruct: Building Instruction Data from Unlabeled Corpus
Shu Chen, Xinyan Guan, Yaojie Lu +3
Manually annotating instruction data for large language models is difficult, costly, and hard to scale. Meanwhile, current automatic annotation methods typically rely on distilling…
On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigation
Xueru Wen, Jie Lou, Xinyu Lu +8
Hallucination occurs when large language models exhibit behavior that deviates from the boundaries of their knowledge during response generation. To address this critical issue, pr…
Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting
Xinyan Guan, Yanjiang Liu, Hongyu Lin +4
Incorporating factual knowledge in knowledge graph is regarded as a promising approach for mitigating the hallucination of large language models (LLMs). Existing methods usually on…
DLUE: Benchmarking Document Language Understanding
Ruoxi Xu, Hongyu Lin, Xinyan Guan +3
Understanding documents is central to many real-world tasks but remains a challenging topic. Unfortunately, there is no well-established consensus on how to comprehensively evaluat…
Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change
Zhaochen Su, Zecheng Tang, Xinyan Guan +3
Recent research has revealed that neural language models at scale suffer from poor temporal generalization capability, i.e., the language model pre-trained on static data from past…