31 citations · 407 across the 77 of their papers we have counts for
78 papers
Beyond Test-Time Compute Strategies: Advocating Energy-per-Token in LLM Inference
Patrick Wilhelm, Thorsten Wittkopp, Odej Kao
Large Language Models (LLMs) demonstrate exceptional performance across diverse tasks but come with substantial energy and computational costs, particularly in request-heavy scenar…
Efficiency Will Not Lead to Sustainable Reasoning AI
Philipp Wiesner, Daniel W. O'Neill, Francesca Larosa +1
AI research is increasingly moving toward complex problem solving, where models are optimized not only for pattern recognition but for multi-step reasoning. Historically, computing…
Comparative Analysis of Large Language Models for the Machine-Assisted Resolution of User Intentions
Justus Flerlage, Alexander Acker, Odej Kao
Large Language Models (LLMs) have emerged as transformative tools for natural language understanding and user intent resolution, enabling tasks such as translation, summarization,…
Empirical Evidences for the Effects of Feature Diversity in Open Set Recognition and Continual Learning
Jiawen Xu, Odej Kao
Open set recognition (OSR) and continual learning are two critical challenges in machine learning, focusing respectively on detecting novel classes at inference time and updating m…
Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations
Miray Özcan, Philipp Wiesner, Philipp Weiß +1
The environmental impact of Large Language Models (LLMs) is rising significantly, with inference now accounting for more than half of their total lifecycle carbon emissions. Howeve…
Moving Beyond Marginal Carbon Intensity: A Poor Metric for Both Carbon Accounting and Grid Flexibility
Philipp Wiesner, Odej Kao
Marginal Carbon Intensity (MCI) has been promoted as an effective metric for carbon-aware computing. Although it is already considered as impractical for carbon accounting purposes…