8 papers
Pretraining EHR Foundation Models with Patient-Aware Sampling
Joshua Placidi, Yuxuan Liu, Jinpei Han +2
Autoregressive foundation models for electronic health records (EHRs) typically inherit pretraining methods from language modeling, where patient trajectories are concatenated into…
DyGnROLE: Asymmetric Pretraining for Edge Classification on Dynamic Graphs
Tyler Bonnet, Marek Rei
Edge classification on directed dynamic graphs requires modeling interactions between source and destination nodes exhibiting asymmetrical behavioral patterns and temporal dynamics…
Capture the Flags: Family-Based Evaluation of Agentic LLMs via Semantics-Preserving Transformations
Shahin Honarvar, Amber Gorzynski, James Lee-Jones +4
Agentic large language models (LLMs) are increasingly evaluated on cybersecurity tasks using capture-the-flag (CTF) benchmarks, yet existing pointwise benchmarks offer limited insi…
TabRAG: Improving Tabular Document Question Answering for Retrieval Augmented Generation via Structured Representations
Jacob Si, Mike Qu, Michelle Lee +2
Incorporating external knowledge bases in traditional retrieval-augmented generation (RAG) relies on parsing the document, followed by querying a language model with the parsed inf…
Fine-tuning with RAG for Improving LLM Learning of New Skills
Humaid Ibrahim, Nikolai Rozanov, Marek Rei
Large language model (LLM) agents deployed for multi-step tasks frequently fail in predictable ways: attempting actions with unmet preconditions, issuing redundant commands, or mis…
DiffuseDef: Improved Robustness to Adversarial Attacks via Iterative Denoising
Zhenhao Li, Huichi Zhou, Marek Rei +1
Pretrained language models have significantly advanced performance across various natural language processing tasks. However, adversarial attacks continue to pose a critical challe…