3 citations · 9 across the 6 of their papers we have counts for
6 papers
MEOW: MEMOry Supervised LLM Unlearning Via Inverted Facts
Tianle Gu, Kexin Huang, Ruilin Luo +4
Large Language Models (LLMs) can memorize sensitive information, raising concerns about potential misuse. LLM Unlearning, a post-hoc approach to remove this information from traine…
MLLMGuard: A Multi-dimensional Safety Evaluation Suite for Multimodal Large Language Models
Tianle Gu, Zeyang Zhou, Kexin Huang +10
Powered by remarkable advancements in Large Language Models (LLMs), Multimodal Large Language Models (MLLMs) demonstrate impressive capabilities in manifold tasks. However, the pra…
ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models
Haiquan Zhao, Lingyu Li, Shisong Chen +11
Emotion Support Conversation (ESC) is a crucial application, which aims to reduce human stress, offer emotional guidance, and ultimately enhance human mental and physical well-bein…
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Chaochao Lu, Chen Qian, Guodong Zheng +33
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide ga…
Flames: Benchmarking Value Alignment of LLMs in Chinese
Kexin Huang, Xiangyang Liu, Qianyu Guo +9
The widespread adoption of large language models (LLMs) across various regions underscores the urgent need to evaluate their alignment with human values. Current benchmarks, howeve…
Fake Alignment: Are LLMs Really Aligned Well?
Yixu Wang, Yan Teng, Kexin Huang +7
The growing awareness of safety concerns in large language models (LLMs) has sparked considerable interest in the evaluation of safety. This study investigates an under-explored is…