50 citations · 81 across the 34 of their papers we have counts for
35 papers
Rethinking the Evaluation of Efficiency Methods for Multi-Agent Systems
Jiamu Zhang, Lingxi Zhang, Pengjun Lu +6
Efficiency is increasingly important for Large Language Model (LLM)-based multi-agent systems (MAS), as larger models and more agents introduce substantial execution costs. Recent…
Right Answer, Wrong Tokens: Tail-Aware Credit Calibration for RLVR
Xiuyi Lou, Zicheng Xu, Yu-Neng Chuang +4
Although reinforcement learning (RL) improves the reasoning capabilities of large language models (LLMs), GRPO-style methods can suffer from a positive-credit contamination failure…
Learning at the Right Pace: Adaptive Data Scheduling Improves LLM Reinforcement Learning
Zicheng Xu, Ruixuan Zhang, Yu-Neng Chuang +7
Large Language Models (LLMs) achieve remarkable reasoning capabilities through reinforcement learning (RL) post-training. However, existing RL post-training commonly relies on unif…
Demystifying OPD: Length Inflation and Stabilization Strategies for Large Language Models
Feng Luo, Yu-Neng Chuang, Guanchu Wang +4
On-policy distillation (OPD) trains student models under their own induced distribution while leveraging supervision from stronger teachers. We identify a failure mode of OPD: as t…
A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data
Kaituo Zhang, Mingzhi Hu, Hoang Anh Duy Le +9
Large Language Models (LLMs) have emerged as powerful tools for generating data across various modalities. By transforming data from a scarce resource into a controllable asset, LL…
DTS: Enhancing Large Reasoning Models via Decoding Tree Sketching
Zicheng Xu, Xiuyi Lou, Guanchu Wang +6
Large Reasoning Models (LRMs) achieve remarkable inference-time improvements through parallel thinking. However, existing methods rely on redundant sampling of reasoning trajectori…
Training-Free Time Series Classification via In-Context Reasoning with LLM Agents
Songyuan Sui, Zihang Xu, Xia Hu
Time series classification (TSC) spans diverse application scenarios, yet labeled data are often scarce, making task-specific training costly and inflexible. Recent reasoning-orien…
Self-ensemble: Mitigating Confidence Mis-calibration for Large Language Models
Zicheng Xu, Guanchu Wang, Guangyao Zheng +4
Although Large Language Models (LLMs) perform well in general fields, they exhibit a confidence distortion problem on multi-choice question-answering (MCQA), particularly as the nu…
AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models
Feng Luo, Yu-Neng Chuang, Guanchu Wang +8
Reasoning-capable large language models (LLMs) achieve strong performance on complex tasks but often exhibit overthinking after distillation, generating unnecessarily long chain-of…
Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
Yang Sui, Yu-Neng Chuang, Guanchu Wang +9
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex tasks. Recent advancements in Large Reasoning Models (LRMs), such as OpenAI o1 and DeepSeek-R1, ha…
Confident or Seek Stronger: Exploring Uncertainty-Based On-device LLM Routing From Benchmarking to Generalization
Yu-Neng Chuang, Leisheng Yu, Guanchu Wang +6
Large language models (LLMs) are increasingly deployed and democratized on edge devices. To improve the efficiency of on-device deployment, small language models (SLMs) are often a…
Learning to Route LLMs with Confidence Tokens
Yu-Neng Chuang, Prathusha Kameswara Sarma, Parikshit Gopalan +4
Large language models (LLMs) have demonstrated impressive performance on several tasks and are increasingly deployed in real-world applications. However, especially in high-stakes…
Taylor Unswift: Secured Weight Release for Large Language Models via Taylor Expansion
Guanchu Wang, Yu-Neng Chuang, Ruixiang Tang +8
Ensuring the security of released large language models (LLMs) poses a significant dilemma, as existing mechanisms either compromise ownership rights or raise data privacy concerns…
Assessing and Enhancing Large Language Models in Rare Disease Question-answering
Guanchu Wang, Junhao Ran, Ruixiang Tang +6
Despite the impressive capabilities of Large Language Models (LLMs) in general medical domains, questions remain about their performance in diagnosing rare diseases. To answer this…
DHP Benchmark: Are LLMs Good NLG Evaluators?
Yicheng Wang, Jiayi Yuan, Yu-Neng Chuang +7
Large Language Models (LLMs) are increasingly serving as evaluators in Natural Language Generation (NLG) tasks; this is often referred to as ``LLM-as-a-judge'' paradigm. However, t…
KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches
Jiayi Yuan, Hongyi Liu, Shaochen Zhong +9
Long context capability is a crucial competency for large language models (LLMs) as it mitigates the human struggle to digest long-form texts. This capability enables complex task-…
GraphFM: A Comprehensive Benchmark for Graph Foundation Model
Yuhao Xu, Xinqi Liu, Keyu Duan +4
Foundation Models (FMs) serve as a general class for the development of artificial intelligence systems, offering broad potential for generalization across a spectrum of downstream…
LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time Series Forecasting
Yu-Neng Chuang, Songchen Li, Jiayi Yuan +11
Time Series Forecasting (TSF) has long been a challenge in time series analysis. Inspired by the success of Large Language Models (LLMs), researchers are now developing Large Time…
Learning to Compress Prompt in Natural Language Formats
Yu-Neng Chuang, Tianwei Xing, Chia-Yuan Chang +3
Large language models (LLMs) are great at processing multiple natural language processing tasks, but their abilities are constrained by inferior performance with long context, slow…
LoRATK: LoRA Once, Backdoor Everywhere in the Share-and-Play Ecosystem
Hongyi Liu, Shaochen Zhong, Xintong Sun +12
Finetuning LLMs with LoRA has gained significant popularity due to its simplicity and effectiveness. Often, users may even find pluggable, community-shared LoRAs to enhance their b…
Feasibility of Identifying Factors Related to Alzheimer's Disease and Related Dementia in Real-World Data
Aokun Chen, Qian Li, Yu Huang +7
A comprehensive view of factors associated with AD/ADRD will significantly aid in studies to develop new treatments for AD/ADRD and identify high-risk populations and patients for…
FaithLM: Towards Faithful Explanations for Large Language Models
Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang +7
Large language models (LLMs) increasingly produce natural language explanations, yet these explanations often lack faithfulness, and they do not reliably reflect the evidence the m…
TVE: Learning Meta-attribution for Transferable Vision Explainer
Guanchu Wang, Yu-Neng Chuang, Fan Yang +8
Explainable machine learning significantly improves the transparency of deep neural networks. However, existing work is constrained to explaining the behavior of individual model p…
DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research
Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang +8
The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are…