2 citations · 2 across the 14 of their papers we have counts for
5 papers · 1 filter
Distilling LLM Agent into Small Models with Retrieval and Code Tools
Minki Kang, Jongwon Jeong, Seanie Lee +2
Large language models (LLMs) excel at complex reasoning tasks but remain computationally expensive, limiting their practical deployment. To address this, recent works have focused…
SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language Models
Seanie Lee, Dong Bok Lee, Dominik Wagner +5
Deploying large language models (LLMs) in real-world applications requires robust safety guard models to detect and block harmful user prompts. While large safety guard models achi…
Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language Models
Minki Kang, Sung Ju Hwang, Gibbeum Lee +1
As Large Language Models (LLMs) are increasingly deployed in specialized domains with continuously evolving knowledge, the need for timely and precise knowledge injection has becom…
HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models
Seanie Lee, Haebin Seong, Dong Bok Lee +6
Safety guard models that detect malicious queries aimed at large language models (LLMs) are essential for ensuring the secure and responsible deployment of LLMs in real-world appli…
Learning diverse attacks on large language models for robust red-teaming and safety tuning
Seanie Lee, Minsu Kim, Lynn Cherif +8
Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of large language models (LLMs). Developing ef…