activity
20242026
most citedTowards Automated Penetration Testing: Introducing LLM Benchmark, Analysis, and Improvements

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

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…

cs.CL2025

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…

cs.CL2024

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…

cs.CR2024★ 1 cited

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…

cs.CR2024

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…