2 citations · 3 across the 4 of their papers we have counts for
4 papers
AURA-Eval: Evaluation Framework for Acting Under Risk Awareness in LLM Agent Trajectories
Ruoxi Shang, Christina-Maria Androna, Orfeas Menis Mastromichalakis +6
LLM agents are increasingly taking actions with real consequences, yet safety evaluations often reduce their behavior to a final safe/unsafe outcome, obscuring whether the agent re…
Lessons from Training Grounded LLMs with Verifiable Rewards
Shang Hong Sim, Tej Deep Pala, Vernon Toh +5
Generating grounded and trustworthy responses remains a key challenge for large language models (LLMs). While retrieval-augmented generation (RAG) with citation-based grounding hol…
Evaluating the Generation of Spatial Relations in Text and Image Generative Models
Shang Hong Sim, Clarence Lee, Alvin Tan +1
Understanding spatial relations is a crucial cognitive ability for both humans and AI. While current research has predominantly focused on the benchmarking of text-to-image (T2I) m…
Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse
Maojia Song, Shang Hong Sim, Rishabh Bhardwaj +3
LLMs are an integral component of retrieval-augmented generation (RAG) systems. While many studies focus on evaluating the overall quality of end-to-end RAG systems, there is a gap…