Context Information
Context information, encompassing the surrounding data influencing a system's response, is a crucial area of research across numerous fields, aiming to improve model accuracy, robustness, and explainability. Current research focuses on how to effectively integrate contextual information into various models, including large language models (LLMs), vision-language models (VLMs), and other machine learning architectures, often employing techniques like retrieval-augmented generation (RAG), attention mechanisms, and contrastive learning. This work is significant because effective contextualization is vital for building reliable and trustworthy AI systems across applications ranging from natural language processing and computer vision to medical diagnosis and autonomous navigation.
Papers
Forecasting Unseen Points of Interest Visits Using Context and Proximity Priors
Ziyao Li, Shang-Ling Hsu, Cyrus Shahabi
Multiverse of Greatness: Generating Story Branches with LLMs
Pittawat Taveekitworachai, Chollakorn Nimpattanavong, Mustafa Can Gursesli, Antonio Lanata, Andrea Guazzini, Ruck Thawonmas
One-Layer Transformer Provably Learns One-Nearest Neighbor In Context
Zihao Li, Yuan Cao, Cheng Gao, Yihan He, Han Liu, Jason M. Klusowski, Jianqing Fan, Mengdi Wang
Enhancing PTSD Outcome Prediction with Ensemble Models in Disaster Contexts
Ayesha Siddiqua, Atib Mohammad Oni, Abu Saleh Musa Miah, Jungpil Shin
Properties of fairness measures in the context of varying class imbalance and protected group ratios
Dariusz Brzezinski, Julia Stachowiak, Jerzy Stefanowski, Izabela Szczech, Robert Susmaga, Sofya Aksenyuk, Uladzimir Ivashka, Oleksandr Yasinskyi
DiVR: incorporating context from diverse VR scenes for human trajectory prediction
Franz Franco Gallo (BIOVISION), Hui-Yin Wu (BIOVISION), Lucile Sassatelli (UniCA, IUF)
Exploring Seasonal Variability in the Context of Neural Radiance Fields for 3D Reconstruction on Satellite Imagery
Liv Kåreborn, Erica Ingerstad, Amanda Berg, Justus Karlsson, Leif Haglund
Lost in Context: The Influence of Context on Feature Attribution Methods for Object Recognition
Sayanta Adhikari, Rishav Kumar, Konda Reddy Mopuri, Rajalakshmi Pachamuthu
Context is Key: A Benchmark for Forecasting with Essential Textual Information
Andrew Robert Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin
A framework for GNSS-based solutions performance analysis in an ERTMS context
Juliette Marais (COSYS-LEOST), Quentin Mayolle (IRT Railenium), Martin Fasquelle (IRT Railenium), Vincent Tardif, Emilie Chéneau-Grehalle