3 papers
cs.CL2026
MTMCS-Bench: Evaluating Contextual Safety of Multimodal Large Language Models in Multi-Turn Dialogues
Zheyuan Liu, Dongwhi Kim, Yixin Wan +4
Multimodal large language models (MLLMs) are increasingly deployed as assistants that interact through text and images, making it crucial to evaluate contextual safety when risk de…
cs.HC2025
From Verification Burden to Trusted Collaboration: Design Goals for LLM-Assisted Literature Reviews
Brenda Nogueira, Werner Geyer, Andrew Anderson +4
Large Language Models (LLMs) are increasingly embedded in academic writing practices. Although numerous studies have explored how researchers employ these tools for scientific writ…
cs.LG2024
Relevance-aware Algorithmic Recourse
Dongwhi Kim, Nuno Moniz
As machine learning continues to gain prominence, transparency and explainability are increasingly critical. Without an understanding of these models, they can replicate and worsen…