With the rapid uptake of generative AI, investigating human perceptions of generated responses has become crucial. A major challenge is their `aptitude' for hallucinating and generating harmful contents. Despite major efforts for implementing guardrails, human perceptions of these mitigation strategies are largely unknown. We conducted a mixed-method experiment for evaluating the responses of a mitigation strategy across multiple-dimensions: faithfulness, fairness, harm-removal capacity, and relevance. In a within-subject study design, 57 participants assessed the responses under two conditions: harmful response plus its mitigation and solely mitigated response. Results revealed that participants' native language, AI work experience, and annotation familiarity significantly influenced evaluations. Participants showed high sensitivity to linguistic and contextual attributes, penalizing minor grammar errors while rewarding preserved semantic contexts. This contrasts with how language is often treated in the quantitative evaluation of LLMs. We also introduced new metrics for training and evaluating mitigation strategies and insights for human-AI evaluation studies.
Exploring the Generalizability of Factual Hallucination Mitigation via Enhancing Precise Knowledge Utilization
Dept. of Comp. Sci. and Tech.（计算机科学与技术系）；Institute for AI（人工智能研究院）；Tsinghua-Bosch Joint ML Center（清华大学-博世联合机器学习中心）；THBI Lab（THBI实验室）；BNRist Center（BNRist中心）；Tsinghua University（清华大学）
LongSpace: Exploring Long-Horizon Spatial Memory from Perception to Recall in Video
Beijing University of Posts and Telecommunications（北京邮电大学）；Zhongguancun Academy（中关村学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；The Chinese University of Hong Kong（香港中文大学）；Xi’an Jiaotong University（西安交通大学）
Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code
2 State Key Laboratory of Complex \& Critical Software Environment (SKLCCSE) School of Computer Science；Engineering, Beihang University, Beijing, China；3 School of Software, Beihang University, Beijing, China；4 School of Computer Science；Technology, Shandong University, Qingdao, China；5 Huawei Cloud Computing Technologies Co., Ltd, China
"I Like Sunnie More Than I Expected!": Exploring User Expectation and Perception of an Anthropomorphic LLM-based Conversational Agent for Well-Being Support
University of Toronto（多伦多大学）；Flourish Science（弗洛里什科学公司）；Northeastern University（东北大学）
Unveiling Transformer Perception by Exploring Input Manifolds
Department of Environmental Science and Policy（环境科学与政策系）；Department of Computer Science（计算机科学系）；Department of Mathematics（数学系）；Department of Control and Computer Engineering（控制与计算机工程系）
Exploring Cultural Nuances in Emotion Perception Across 15 African Languages
Northeastern University（东北大学）；Bayero University Kano（卡诺州巴耶罗大学）；Instituto Politécnico Nacional（墨西哥国立理工学院）；Wollo University（沃洛大学）；University of Pretoria（比勒陀利亚大学）；University of Hamburg（汉堡大学）；Imperial College London（伦敦帝国学院）
