10 citations · 11 across the 8 of their papers we have counts for
9 papers
Transfer Learning for Edge Classification on Dynamic Text-Attributed Graphs
Tyler Bonnet, Marek Rei
Learning transferable representations for dynamic text-attributed graphs (DyTAGs) requires models to capture underlying interaction dynamics that persist across domains. However, e…
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…
An Analysis of Role Semantics in Directed Dynamic Graph Modeling for Edge Classification
Tyler Bonnet, Marek Rei
Edge classification on directed dynamic graphs is critical for understanding evolving user-item interactions and behavioral patterns in recommender systems and social platforms. Ex…
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…
StateAct: Enhancing LLM Base Agents via Self-prompting and State-tracking
Nikolai Rozanov, Marek Rei
Large language models (LLMs) are increasingly used as autonomous agents, tackling tasks from robotics to web navigation. Their performance depends on the underlying base agent. Exi…
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…
Prompting open-source and commercial language models for grammatical error correction of English learner text
Christopher Davis, Andrew Caines, Øistein Andersen +6
Thanks to recent advances in generative AI, we are able to prompt large language models (LLMs) to produce texts which are fluent and grammatical. In addition, it has been shown tha…