{"total":147,"rows":[{"id":"2505.09598","title":"How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference","created":"2025-05-14","cited_by_count":76,"area":null},{"id":"2404.00579","title":"A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)","created":"2024-03-31","cited_by_count":58,"area":null},{"id":"2504.17674","title":"Energy Considerations of Large Language Model Inference and Efficiency Optimizations","created":"2025-04-24","cited_by_count":55,"area":null},{"id":"2501.17047","title":"How Linguistics Learned to Stop Worrying and Love the Language Models","created":"2025-01-28","cited_by_count":51,"area":null},{"id":"2502.02649","title":"Fully Autonomous AI Agents Should Not be Developed","created":"2025-02-04","cited_by_count":45,"area":null},{"id":"2405.19479","title":"Participation in the age of foundation models","created":"2024-05-29","cited_by_count":32,"area":null},{"id":"2503.05804","title":"Holistically Evaluating the Environmental Impact of Creating Language Models","created":"2025-03-03","cited_by_count":26,"area":null},{"id":"2407.16893","title":"The Price of Prompting: Profiling Energy Use in Large Language Models Inference","created":"2024-07-04","cited_by_count":23,"area":null},{"id":"2509.10147","title":"Virtual Agent Economies","created":"2025-09-12","cited_by_count":21,"area":null},{"id":"2507.18553","title":"The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm","created":"2025-07-24","cited_by_count":20,"area":null},{"id":"2410.02950","title":"LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences","created":"2024-10-03","cited_by_count":19,"area":null},{"id":"2501.12557","title":"Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review","created":"2025-01-22","cited_by_count":16,"area":null},{"id":"2408.09966","title":"Mask in the Mirror: Implicit Sparsification","created":"2024-08-19","cited_by_count":14,"area":null},{"id":"2401.00284","title":"Evaluation is all you need. Prompting Generative Large Language Models for Annotation Tasks in the Social Sciences. A Primer using Open Models","created":"2023-12-30","cited_by_count":12,"area":null},{"id":"2406.01352","title":"Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience","created":"2024-06-03","cited_by_count":11,"area":null},{"id":"2502.05610","title":"Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models","created":"2025-02-08","cited_by_count":11,"area":null},{"id":"2504.06667","title":"Toward Holistic Evaluation of Recommender Systems Powered by Generative Models","created":"2025-04-09","cited_by_count":11,"area":null},{"id":"2312.09634","title":"Vectorizing string entries for data processing on tables: when are larger language models better?","created":"2023-12-15","cited_by_count":10,"area":null},{"id":"2505.20276","title":"Does quantization affect models' performance on long-context tasks?","created":"2025-05-26","cited_by_count":10,"area":null},{"id":"2412.16156","title":"Personalized Representation from Personalized Generation","created":"2024-12-20","cited_by_count":9,"area":null},{"id":"2506.08686","title":"Brevity is the soul of sustainability: Characterizing LLM response lengths","created":"2025-06-10","cited_by_count":9,"area":null},{"id":"2411.13055","title":"Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training","created":"2024-11-20","cited_by_count":8,"area":null},{"id":"2506.01774","title":"Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices","created":"2025-06-02","cited_by_count":8,"area":null},{"id":"2509.22092","title":"Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements","created":"2025-09-26","cited_by_count":8,"area":null},{"id":"2405.02385","title":"Efficient Deep Learning with Decorrelated Backpropagation","created":"2024-05-03","cited_by_count":7,"area":null},{"id":"2505.01386","title":"CATransformers: Carbon Aware Transformers Through Joint Model-Hardware Optimization","created":"2025-05-02","cited_by_count":7,"area":null},{"id":"2507.03257","title":"LACONIC: A 3D Layout Adapter for Controllable Image Creation","created":"2025-07-04","cited_by_count":6,"area":null},{"id":"2507.20301","title":"Advancing Dialectal Arabic to Modern Standard Arabic Machine Translation","created":"2025-07-27","cited_by_count":6,"area":null},{"id":"2404.04549","title":"Stable Learning Using Spiking Neural Networks Equipped With Affine Encoders and Decoders","created":"2024-04-06","cited_by_count":5,"area":null},{"id":"2409.07471","title":"AI, Climate, and Transparency: Operationalizing and Improving the AI Act","created":"2024-08-28","cited_by_count":4,"area":null},{"id":"2410.06681","title":"AI, Climate, and Regulation: From Data Centers to the AI Act","created":"2024-10-09","cited_by_count":4,"area":null},{"id":"2502.18424","title":"Compressing Language Models for Specialized Domains","created":"2025-02-25","cited_by_count":4,"area":null},{"id":"2511.05613","title":"Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations","created":"2025-11-06","cited_by_count":4,"area":null},{"id":"2601.22362","title":"Understanding Efficiency: Quantization, Batching, and Serving Strategies in LLM Energy Use","created":"2026-01-29","cited_by_count":4,"area":null},{"id":"2406.18142","title":"Innovating for Tomorrow: The Convergence of SE and Green AI","created":"2024-06-26","cited_by_count":3,"area":null},{"id":"2410.13114","title":"Sound Check: Auditing Audio Datasets","created":"2024-10-17","cited_by_count":3,"area":null},{"id":"2410.15509","title":"Exploring Curriculum Learning for Vision-Language Tasks: A Study on Small-Scale Multimodal Training","created":"2024-10-20","cited_by_count":3,"area":null},{"id":"2503.10666","title":"Green Prompting: Characterizing Prompt-driven Energy Costs of LLM Inference","created":"2025-03-09","cited_by_count":3,"area":null},{"id":"2509.19222","title":"Video Killed the Energy Budget: Characterizing the Latency and Power Regimes of Open Text-to-Video Models","created":"2025-09-23","cited_by_count":3,"area":null},{"id":"2510.01889","title":"Small is Sufficient: Reducing the World AI Energy Consumption Through Model Selection","created":"2025-10-02","cited_by_count":3,"area":null},{"id":"2511.13703","title":"Generalist Foundation Models Are Not Clinical Enough for Hospital Operations","created":"2025-11-17","cited_by_count":3,"area":null},{"id":"2512.08946","title":"Large Language Models as Search Engines: Societal Challenges","created":"2025-11-24","cited_by_count":3,"area":null},{"id":"2607.26571","title":"From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs","created":"2026-07-29","cited_by_count":3,"area":null},{"id":"2404.08727","title":"Can LLMs substitute SQL? Comparing Resource Utilization of Querying LLMs versus Traditional Relational Databases","created":"2024-04-12","cited_by_count":2,"area":null},{"id":"2404.15211","title":"LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demand","created":"2024-03-29","cited_by_count":2,"area":null},{"id":"2409.12878","title":"Impact of ML Optimization Tactics on Greener Pre-Trained ML Models","created":"2024-09-19","cited_by_count":2,"area":null},{"id":"2501.17980","title":"Limits to AI Growth: The Ecological and Social Consequences of Scaling","created":"2025-01-29","cited_by_count":2,"area":null},{"id":"2502.07192","title":"OscNet: Machine Learning on CMOS Oscillator Networks","created":"2025-02-11","cited_by_count":2,"area":null},{"id":"2504.15829","title":"Generative AI for Research Data Processing: Lessons Learnt From Three Use Cases","created":"2025-04-22","cited_by_count":2,"area":null},{"id":"2506.19484","title":"Dialogic Pedagogy for Large Language Models: Aligning Conversational AI with Proven Theories of Learning","created":"2025-06-24","cited_by_count":2,"area":null},{"id":"2508.08672","title":"Imposing AI: Deceptive design patterns against sustainability","created":"2025-08-12","cited_by_count":2,"area":null},{"id":"2512.04142","title":"From FLOPs to Footprints: The Resource Cost of Artificial Intelligence","created":"2025-12-03","cited_by_count":2,"area":null},{"id":"2602.05712","title":"Towards Green AI: Decoding the Energy of LLM Inference in Software Development","created":"2026-02-05","cited_by_count":2,"area":null},{"id":"2606.11215","title":"The Environmental Cost of LLMs in AIED: Reporting and Practices","created":"2026-05-03","cited_by_count":2,"area":null},{"id":"2606.15444","title":"A Conservation Law for Equilibrium Propagation and Coupled Learning","created":"2026-06-13","cited_by_count":2,"area":null},{"id":"2607.16183","title":"A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing","created":"2026-07-17","cited_by_count":2,"area":null},{"id":"2406.02773","title":"Cyclic Sparse Training: Is it Enough?","created":"2024-06-04","cited_by_count":1,"area":null},{"id":"2408.00025","title":"Need of AI in Modern Education: in the Eyes of Explainable AI (xAI)","created":"2024-07-31","cited_by_count":1,"area":null},{"id":"2409.00447","title":"The MERIT Dataset: Modelling and Efficiently Rendering Interpretable Transcripts","created":"2024-08-31","cited_by_count":1,"area":null},{"id":"2410.10665","title":"Double Jeopardy and Climate Impact in the Use of Large Language Models: Socio-economic Disparities and Reduced Utility for Non-English Speakers","created":"2024-10-14","cited_by_count":1,"area":null},{"id":"2410.19812","title":"Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models","created":"2024-10-17","cited_by_count":1,"area":null},{"id":"2501.16061","title":"The Unbearable Lightness of Prompting: A Critical Reflection on the Environmental Impact of genAI use in Design Education","created":"2025-01-27","cited_by_count":1,"area":null},{"id":"2502.16701","title":"Beyond Release: Access Considerations for Generative AI Systems","created":"2025-02-23","cited_by_count":1,"area":null},{"id":"2505.07317","title":"How Do Companies Manage the Environmental Sustainability of AI? An Interview Study About Green AI Efforts and Regulations","created":"2025-05-12","cited_by_count":1,"area":null},{"id":"2506.12610","title":"OscNet v1.5: Energy Efficient Hopfield Network on CMOS Oscillators for Image Classification","created":"2025-06-14","cited_by_count":1,"area":null},{"id":"2506.13838","title":"Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy","created":"2025-06-16","cited_by_count":1,"area":null},{"id":"2508.14170","title":"Comparing energy consumption and accuracy in text classification inference","created":"2025-08-19","cited_by_count":1,"area":null},{"id":"2509.04506","title":"Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENs","created":"2025-09-02","cited_by_count":1,"area":null},{"id":"2509.18962","title":"Lift What You Can: Green Online Learning with Heterogeneous Ensembles","created":"2025-09-23","cited_by_count":1,"area":null},{"id":"2510.11119","title":"Improving AI Efficiency in Data Centres by Power Dynamic Response","created":"2025-10-13","cited_by_count":1,"area":null},{"id":"2511.07436","title":"Analysing Environmental Efficiency in AI for X-Ray Diagnosis","created":"2025-10-31","cited_by_count":1,"area":null},{"id":"2511.21668","title":"Through the telecom lens: Are all training samples important?","created":"2025-11-26","cited_by_count":1,"area":null},{"id":"2512.03077","title":"Irresponsible AI: big tech's influence on AI research and associated impacts","created":"2025-11-27","cited_by_count":1,"area":null},{"id":"2512.23065","title":"TabiBERT: A Large-Scale ModernBERT Foundation Model and A Unified Benchmark for Turkish","created":"2025-12-28","cited_by_count":1,"area":null},{"id":"2601.14901","title":"Just aware enough: Evaluating awareness across artificial systems","created":"2026-01-21","cited_by_count":1,"area":null},{"id":"2601.22357","title":"Small Talk, Big Impact: The Energy Cost of Thanking AI","created":"2026-01-29","cited_by_count":1,"area":null},{"id":"2602.00056","title":"How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI","created":"2026-01-20","cited_by_count":1,"area":null},{"id":"2602.17697","title":"Pimp My LLM: Leveraging Variability Modeling to Tune Inference Hyperparameters","created":"2026-02-06","cited_by_count":1,"area":null},{"id":"2603.00078","title":"Alignment Is Not Enough: A Relational Framework for Moral Standing in Human-AI Interaction","created":"2026-02-13","cited_by_count":1,"area":null},{"id":"2604.21637","title":"Multilinguality at the Edge: Developing Language Models for the Global South","created":"2026-04-23","cited_by_count":1,"area":null},{"id":"2605.03090","title":"From Barrier to Bridge: The Case for AI Data Center/Power Grid Co-Design","created":"2026-05-04","cited_by_count":1,"area":null},{"id":"2607.09172","title":"Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations","created":"2026-07-10","cited_by_count":1,"area":null},{"id":"2608.31108","title":"Stress-Testing Efficient Responsible-AI Evaluation: When Compute Savings Change Benchmark Conclusions","created":"2026-08-31","cited_by_count":1,"area":null},{"id":"2407.18017","title":"A Sensitivity Analysis of Cellular Automata and Heterogeneous Topology Networks: Partially-Local Cellular Automata and Homogeneous Homogeneous Random Boolean Networks","created":"2024-07-25","cited_by_count":0,"area":null},{"id":"2412.08520","title":"GR-NLP-TOOLKIT: An Open-Source NLP Toolkit for Modern Greek","created":"2024-12-11","cited_by_count":0,"area":null},{"id":"2501.07991","title":"Training Hybrid Neural Networks with Multimode Optical Nonlinearities Using Digital Twins","created":"2025-01-14","cited_by_count":0,"area":null},{"id":"2501.11909","title":"Bridging the Communication Gap: Evaluating AI Labeling Practices for Trustworthy AI Development","created":"2025-01-21","cited_by_count":0,"area":null},{"id":"2502.06693","title":"Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium","created":"2025-02-10","cited_by_count":0,"area":null},{"id":"2503.22810","title":"Harnessing uncertainty when learning through Equilibrium Propagation in neural networks","created":"2025-03-28","cited_by_count":0,"area":null},{"id":"2504.00692","title":"The HCI GenAI CO2ST Calculator: A Tool for Calculating the Carbon Footprint of Generative AI Use in Human-Computer Interaction Research","created":"2025-04-01","cited_by_count":0,"area":null},{"id":"2505.21720","title":"Responsible Data Stewardship: Generative AI and the Digital Waste Problem","created":"2025-05-27","cited_by_count":0,"area":null},{"id":"2507.09080","title":"BioAnalyst: A Foundation Model for Biodiversity","created":"2025-07-11","cited_by_count":0,"area":null},{"id":"2507.10750","title":"AI and the Net-Zero Journey: Energy Demand, Emissions, and the Potential for Transition","created":"2025-07-14","cited_by_count":0,"area":null},{"id":"2507.22921","title":"Fast and Accurate Contextual Knowledge Extraction Using Cascading Language Model Chains and Candidate Answers","created":"2025-07-21","cited_by_count":0,"area":null},{"id":"2510.14657","title":"Decorrelation Speeds Up Vision Transformers","created":"2025-10-16","cited_by_count":0,"area":null},{"id":"2511.04776","title":"Quantifying the Climate Risk of Generative AI: Region-Aware Carbon Accounting with G-TRACE and the AI Sustainability Pyramid","created":"2025-11-06","cited_by_count":0,"area":null},{"id":"2511.09788","title":"Why Open Small AI Models Matter for Interactive Art","created":"2025-11-12","cited_by_count":0,"area":null},{"id":"2512.03054","title":"Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research","created":"2025-11-25","cited_by_count":0,"area":null},{"id":"2512.09394","title":"Language models as tools for investigating the distinction between possible and impossible natural languages","created":"2025-12-10","cited_by_count":0,"area":null},{"id":"2512.12447","title":"Large language models have learned to use language","created":"2025-12-13","cited_by_count":0,"area":null}],"next_cursor":"100"}