As Large Language Models (LLMs) become increasingly sophisticated and ubiquitous in natural language processing (NLP) applications, ensuring their robustness, trustworthiness, and alignment with human values has become a critical challenge. This paper presents a novel framework for contextual grounding in textual models, with a particular emphasis on the Context Representation stage. Our approach aims to enhance the reliability and ethical alignment of these models through a comprehensive, context-aware methodology. By explicitly capturing and representing relevant situational, cultural, and ethical contexts in a machine-readable format, we lay the foundation for anchoring a model's behavior within these contexts. Our approach leverages techniques from knowledge representation and reasoning, such as ontologies, semantic web technologies, and logic-based formalisms. We evaluate our framework on real-world textual datasets, demonstrating its effectiveness in improving model performance, fairness, and alignment with human expectations, while maintaining high accuracy. Furthermore, we discuss the other key components of the framework, including context-aware encoding, context-aware learning, interpretability and explainability, and continuous monitoring and adaptation. This research contributes to the growing body of work on responsible AI, offering a practical approach to developing more reliable, trustworthy, and ethically-aligned language models. Our findings have significant implications for the deployment of LLMs in sensitive domains such as healthcare, legal systems, and social services, where contextual understanding is paramount.
MUPA: Towards Multi-Path Agentic Reasoning for Grounded Video Question Answering
Lanzhou University（兰州大学）；National University of Singapore（新加坡国立大学）；Sun Yat-sen University（中山大学）；Jinan University（暨南大学）；Nanyang Technological University（南洋理工大学）；Peking University（北京大学）；University of Science and Technology of China（中国科学技术大学）；Hefei University of Technology（合肥工业大学）
DWTSumm: Discrete Wavelet Transform for Document Summarization
School of Engineering and Applied Science, George Washington University（乔治华盛顿大学工程与应用科学学院）；Faculty of Computers and Artificial Intelligence, Cairo University（开罗大学计算机与人工智能学院）；Language Technologies Institute, Carnegie Mellon University（卡内基梅隆大学语言技术研究所）
P$^2$-DPO: Grounding Hallucination in Perceptual Processing via Calibration Direct Preference Optimization
Guangdong Provincial Key Laboratory of Computational AI Models and Cognitive Intelligence, School of Computer Science & Engineering, South China University of Technology（广东省计算人工智能模型与认知智能重点实验室，计算机科学与工程学院，华南理工大学）；Pazhou Lab, Guangzhou, China（琶洲实验室，广州，中国）；Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human, Guangzhou, China（教育部健康智能感知与并行数字人工程研究中心，广州，中国）
A Visual Semantic Adaptive Watermark grounded by Prefix-Tuning for Large Vision-Language Model
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Zhejiang University（浙江大学）
Taming a Retrieval Framework to Read Images in Humanlike Manner for Augmenting Generation of MLLMs
Emory University（埃默里大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Illinois Chicago（伊利诺伊大学香槟分校）；The Hong Kong Polytechnic University（香港理工大学）
MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning
National University of Singapore（新加坡国立大学）；The Chinese University of Hong Kong（香港中文大学）；Hunan University（湖南大学）；Xi’an Jiaotong University（西安交通大学）；Guangdong Provincial People’s Hospital（广东省人民医院）
University of California Santa Barbara（加州大学圣巴巴拉分校）
Data-to-text generation models face challenges in ensuring data fidelity by referring to the correct input source. To inspire studies in this area, Wiseman et al. (2017) introduced the RotoWire corpus on generating NBA game summaries from the box- and line-score tables. However, limited attempts have been made in this direction and the challenges remain. We observe a prominent bottleneck in the corpus where only about 60% of the summary contents can be grounded to the boxscore records. Such information deficiency tends to misguide a conditioned language model to produce unconditioned random fa…
The Need for an External Observer Formalizing the Sufficiency Gap: A Mathematical Extension of Mixture Identifiability and Contextual Grounding in Sequence Models
The ability to map descriptions of scenes to 3D geometric representations has many applications in areas such as art, education, and robotics. However, prior work on the text to 3D scene generation task has used manually specified object categories and language that identifies them. We introduce a dataset of 3D scenes annotated with natural language descriptions and learn from this data how to ground textual descriptions to physical objects. Our method successfully grounds a variety of lexical terms to concrete referents, and we show quantitatively that our method improves 3D scene generation …
Evaluating Reasoning Faithfulness in Medical Vision-Language Models using Multimodal Perturbations
Technical University of Munich (TUM)（慕尼黑技术大学）；TUM University Hospital（慕尼黑技术大学医院）；German Heart Center TUM University Hospital（慕尼黑技术大学医院德国心脏中心）；Department of Radiology（放射科）；Klinikum rechts der Isar TUM University Hospital（慕尼黑技术大学医院右岸诊所）；Uniklinik RWTH Aachen（亚琛工业大学医院）；HOPPR IL USA（HOPPR美国）；University of Oxford（牛津大学）；Imperial College London（伦敦帝国学院）
