1 citations · 1 across the 6 of their papers we have counts for
6 papers
Conformal Prediction for Risk-Controlled Medical Entity Extraction Across Clinical Domains
Manil Shrestha, Edward Kim
Large Language Models (LLMs) are increasingly used for medical entity extraction, yet their confidence scores are often miscalibrated, limiting safe deployment in clinical settings…
Efficient Multi-Hop Question Answering over Knowledge Graphs via LLM Planning and Embedding-Guided Search
Manil Shrestha, Edward Kim
Multi-hop question answering over knowledge graphs remains computationally challenging due to the combinatorial explosion of possible reasoning paths. Recent approaches rely on exp…
Structured Extraction of Real World Medical Knowledge using LLMs for Summarization and Search
Edward Kim, Manil Shrestha, Richard Foty +2
Creation and curation of knowledge graphs can accelerate disease discovery and analysis in real-world data. While disease ontologies aid in biological data annotation, codified cat…
Towards Automated Penetration Testing: Introducing LLM Benchmark, Analysis, and Improvements
Isamu Isozaki, Manil Shrestha, Rick Console +1
Hacking poses a significant threat to cybersecurity, inflicting billions of dollars in damages annually. To mitigate these risks, ethical hacking, or penetration testing, is employ…
Secure Multiparty Generative AI
Manil Shrestha, Yashodha Ravichandran, Edward Kim
As usage of generative AI tools skyrockets, the amount of sensitive information being exposed to these models and centralized model providers is alarming. For example, confidential…
E3: Ensemble of Expert Embedders for Adapting Synthetic Image Detectors to New Generators Using Limited Data
Aref Azizpour, Tai D. Nguyen, Manil Shrestha +3
As generative AI progresses rapidly, new synthetic image generators continue to emerge at a swift pace. Traditional detection methods face two main challenges in adapting to these…