5 citations · 7 across the 8 of their papers we have counts for
11 papers
FORGE: Verification-Gated Behavioral Repair for Generative Language Models
Hsin-Ling Hsu, Min-Yu Chen, Nai-Chia Chen +3
Generative large language models (LLMs) inherit undesirable behaviors from pre-training, including demographic bias and toxic generation, that often emerge only after deployment an…
Do LLMs Understand Sequential Structure? A Controlled Study of Inference and Generation
Jerry Wang, Zhengxiang Wang, Ting Yu Liu +3
Large language models (LLMs) are increasingly used as interactive agents and simulators, yet it remains unclear whether they can recover latent sequential structure beyond surface…
When Successful Knowledge Graph Edits Displace Correct Answers: Rank-Level Locality beyond Parameter Support
Yi-Cheng Lai, Jerry Wang, Hsin-Ling Hsu +3
Editing a knowledge graph embedding (KGE) model to promote a desired answer can displace correct answers from the returned list. Locality tests based only on facts that reuse the e…
DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening
Yung Wei Shueh, Zhi-Jie Chen, Chia-Hsuan Hsu +9
Large language models (LLMs) offer promising clinical decision support but remain vulnerable to hallucinated facts, unsupported recommendations, and citation errors. We present DIA…
OTTER: A Red-Teaming System for Toxicity-Evading Jailbreak Prompt Optimization
Jerry Wang, Hsin-Ling Hsu, Yi-Cheng Lai +2
Production LLMs increasingly rely on toxicity-based moderation filters as a primary defense, assuming that harmful intent correlates with toxic surface wording. We show this assump…
MedAction: Towards Active Multi-turn Clinical Diagnostic LLMs
Hsin-Ling Hsu, Zizheng Wang, Donghua Zhang +9
Most existing LLM diagnoses are evaluated on static, single-turn settings where complete patient information is provided upfront, an oversimplification of real clinical practice. W…
WARP: Guaranteed Inner-Layer Repair of NLP Transformers
Hsin-Ling Hsu, Min-Yu Chen, Nai-Chia Chen +3
Transformer-based NLP models remain vulnerable to adversarial perturbations, yet existing repair methods face a fundamental trade-off: gradient-based approaches offer flexibility b…
RPRO: Ranked Preference Reinforcement Optimization for Enhancing Medical QA and Diagnostic Reasoning
Chia-Hsuan Hsu, Jun-En Ding, Hsin-Ling Hsu +5
Medical question answering requires advanced reasoning that integrates domain knowledge with logical inference. However, existing large language models (LLMs) often generate reason…
KAP: MLLM-assisted OCR Text Enhancement for Hybrid Retrieval in Chinese Non-Narrative Documents
Hsin-Ling Hsu, Ping-Sheng Lin, Jing-Di Lin +1
Hybrid Retrieval systems, combining Sparse and Dense Retrieval methods, struggle with Traditional Chinese non-narrative documents due to their complex formatting, rich vocabulary,…
DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generation
Hsin-Ling Hsu, Jengnan Tzeng
Hybrid retrieval techniques in Retrieval-Augmented Generation (RAG) systems enhance information retrieval by combining dense and sparse (e.g., BM25-based) retrieval methods. Howeve…
MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation
Hsin-Ling Hsu, Cong-Tinh Dao, Luning Wang +12
Despite recent success in applying large language models (LLMs) to electronic health records (EHR), most systems focus primarily on assessment rather than treatment planning. We id…