1 citations · 1 across the 9 of their papers we have counts for
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Mitigating Over-Personalization in LLMs via Structured Memory
Hakeem Hannoon, Andrew Zhao, Mihir Narayan +2
Conversational assistants increasingly rely on persistent long-term memory to personalize responses across sessions. However, when stored user information is reintroduced into the…
PersistBench: When Should Long-Term Memories Be Forgotten by LLMs?
Sidharth Pulipaka, Oliver Chen, Manas Sharma +3
Conversational assistants are increasingly integrating long-term memory with large language models (LLMs). This persistence of memories, e.g., the user is vegetarian, can enhance p…
Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution
Ruta Binkyte, Ivaxi Sheth, Zhijing Jin +3
As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), are increasingly deployed in high-stakes domains, ensuring their trustworthines…
Safety Must Precede the Deployment of Open-Ended AI
Ivaxi Sheth, Jan Wehner, Sahar Abdelnabi +2
AI advancements have been significantly driven by a combination of foundation models and curiosity-driven learning aimed at increasing capability and adaptability. Within this land…
IV Co-Scientist: Multi-Agent LLM Framework for Causal Instrumental Variable Discovery
Ivaxi Sheth, Zhijing Jin, Bryan Wilder +2
In the presence of confounding between an endogenous variable and the outcome, instrumental variables (IVs) are used to isolate the causal effect of the endogenous variable. Identi…
MedG-KRP: Medical Graph Knowledge Representation Probing
Gabriel R. Rosenbaum, Lavender Yao Jiang, Ivaxi Sheth +11
Large language models (LLMs) have recently emerged as powerful tools, finding many medical applications. LLMs' ability to coalesce vast amounts of information from many sources to…