External Influence
External influence, encompassing how various factors impact systems and processes, is a burgeoning research area with applications across diverse fields. Current studies focus on quantifying and mitigating the influence of biases in large language models, noise in image processing, and user preferences in recommender systems, often employing techniques like deep learning, Gaussian filtering, and stochastic simulation. Understanding and controlling external influences is crucial for developing robust, fair, and reliable AI systems and improving the accuracy and efficiency of various technologies, from medical image analysis to autonomous vehicles.
Papers
We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields
Jan Philip Wahle, Terry Ruas, Mohamed Abdalla, Bela Gipp, Saif M. Mohammad
Unveiling the Multi-Annotation Process: Examining the Influence of Annotation Quantity and Instance Difficulty on Model Performance
Pritam Kadasi, Mayank Singh
Influence of Team Interactions on Multi-Robot Cooperation: A Relational Network Perspective
Yasin Findik, Hamid Osooli, Paul Robinette, Kshitij Jerath, S. Reza Ahmadzadeh
Affective Conversational Agents: Understanding Expectations and Personal Influences
Javier Hernandez, Jina Suh, Judith Amores, Kael Rowan, Gonzalo Ramos, Mary Czerwinski