2 citations · 2 across the 11 of their papers we have counts for
6 papers · 1 filter
Risk-Averse Constrained Reinforcement Learning with Optimized Certainty Equivalents
Jane H. Lee, Baturay Saglam, Spyridon Pougkakiotis +2
Constrained optimization provides a common framework for dealing with conflicting objectives in reinforcement learning (RL). In most of these settings, the objectives (and constrai…
Think Before You Retrieve: Learning Test-Time Adaptive Search with Small Language Models
Supriti Vijay, Aman Priyanshu, Anu Vellore +2
Effective information retrieval requires reasoning over partial evidence and refining strategies as information emerges. Yet current approaches fall short: neural retrievers lack r…
Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report
Sajana Weerawardhena, Paul Kassianik, Blaine Nelson +14
Large language models (LLMs) have shown remarkable success across many domains, yet their integration into cybersecurity applications remains limited due to a lack of general-purpo…
Large Language Models Encode Semantics and Alignment in Linearly Separable Representations
Baturay Saglam, Paul Kassianik, Blaine Nelson +3
Understanding the latent space geometry of large language models (LLMs) is key to interpreting their behavior and improving alignment. Yet it remains unclear to what extent LLMs li…
Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report
Paul Kassianik, Baturay Saglam, Alexander Chen +15
As transformer-based large language models (LLMs) increasingly permeate society, they have revolutionized domains such as software engineering, creative writing, and digital arts.…
Learning Task Representations from In-Context Learning
Baturay Saglam, Xinyang Hu, Zhuoran Yang +2
Large language models (LLMs) have demonstrated remarkable proficiency in in-context learning (ICL), where models adapt to new tasks through example-based prompts without requiring…