2 citations · 3 across the 5 of their papers we have counts for
6 papers
TCMQA: A 38K-Question Traditional Chinese Medicine Benchmark with a Licensed-Practitioner Reference
Tzu-Heng Huang, Jet Lin, Eric Lin
Medical benchmarks for language models are built almost entirely on Western biomedicine. Traditional Chinese Medicine (TCM) is a separate system, with its own diagnostic framework…
Shallow Robustness, Deep Vulnerabilities: Multi-Turn Evaluation of Medical LLMs
Blazej Manczak, Eric Lin, Francisco Eiras +2
Large language models (LLMs) are rapidly transitioning into medical clinical use, yet their reliability under realistic, multi-turn interactions remains poorly understood. Existing…
Unified Multi-Task Learning & Model Fusion for Efficient Language Model Guardrailing
James O' Neill, Santhosh Subramanian, Eric Lin +1
The trend towards large language models (LLMs) for guardrailing against undesired behaviors is increasing and has shown promise for censoring user inputs. However, increased latenc…
Know Thy Judge: On the Robustness Meta-Evaluation of LLM Safety Judges
Francisco Eiras, Eliott Zemour, Eric Lin +1
Large Language Model (LLM) based judges form the underpinnings of key safety evaluation processes such as offline benchmarking, automated red-teaming, and online guardrailing. This…
PrimeGuard: Safe and Helpful LLMs through Tuning-Free Routing
Blazej Manczak, Eliott Zemour, Eric Lin +1
Deploying language models (LMs) necessitates outputs to be both high-quality and compliant with safety guidelines. Although Inference-Time Guardrails (ITG) offer solutions that shi…
Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection
Taehyeon Kim, Eric Lin, Junu Lee +2
Federated Learning (FL) has emerged as a potent framework for training models across distributed data sources while maintaining data privacy. Nevertheless, it faces challenges with…