{"total":184,"rows":[{"id":"2211.02001","title":"Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model","created":"2022-11-03","cited_by_count":265,"area":null},{"id":"2304.01433","title":"TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings","created":"2023-04-04","cited_by_count":202,"area":null},{"id":"2311.16863","title":"Power Hungry Processing: Watts Driving the Cost of AI Deployment?","created":"2023-11-28","cited_by_count":191,"area":null},{"id":"2309.10621","title":"Large language models can accurately predict searcher preferences","created":"2023-09-19","cited_by_count":131,"area":null},{"id":"2206.05229","title":"Measuring the Carbon Intensity of AI in Cloud Instances","created":"2022-06-10","cited_by_count":111,"area":null},{"id":"2502.02542","title":"OverThink: Slowdown Attacks on Reasoning LLMs","created":"2025-02-04","cited_by_count":96,"area":"lr"},{"id":"2305.11391","title":"A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation","created":"2023-05-19","cited_by_count":93,"area":null},{"id":"2301.11047","title":"A Systematic Review of Green AI","created":"2023-01-26","cited_by_count":77,"area":null},{"id":"2309.14393","title":"LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models","created":"2023-09-25","cited_by_count":72,"area":"moe"},{"id":"2303.11568","title":"Large AI Models in Health Informatics: Applications, Challenges, and the Future","created":"2023-03-21","cited_by_count":61,"area":null},{"id":"2305.02231","title":"Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation","created":"2023-05-02","cited_by_count":61,"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":"2207.04630","title":"On the Principles of Parsimony and Self-Consistency for the Emergence of Intelligence","created":"2022-07-11","cited_by_count":50,"area":"lr"},{"id":"2304.03271","title":"Making AI Less \"Thirsty\": Uncovering and Addressing the Secret Water Footprint of AI Models","created":"2023-04-06","cited_by_count":44,"area":null},{"id":"2302.08476","title":"Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning","created":"2023-02-16","cited_by_count":42,"area":null},{"id":"2307.06440","title":"No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models","created":"2023-07-12","cited_by_count":41,"area":null},{"id":"2210.06640","title":"Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities","created":"2022-10-13","cited_by_count":38,"area":null},{"id":"2403.12844","title":"MELTing point: Mobile Evaluation of Language Transformers","created":"2024-03-19","cited_by_count":36,"area":null},{"id":"2412.13879","title":"Crabs: Consuming Resource via Auto-generation for LLM-DoS Attack under Black-box Settings","created":"2024-12-18","cited_by_count":35,"area":null},{"id":"2508.15734","title":"Measuring the environmental impact of delivering AI at Google Scale","created":"2025-08-21","cited_by_count":32,"area":null},{"id":"2503.15848","title":"Entropy-based Exploration Conduction for Multi-step Reasoning","created":"2025-03-20","cited_by_count":29,"area":"lr"},{"id":"2208.12852","title":"What Do NLP Researchers Believe? Results of the NLP Community Metasurvey","created":"2022-08-26","cited_by_count":28,"area":null},{"id":"2305.03123","title":"ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review","created":"2023-04-13","cited_by_count":28,"area":null},{"id":"2207.07697","title":"POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging","created":"2022-07-15","cited_by_count":26,"area":null},{"id":"2507.19227","title":"Jailbreaking Large Language Diffusion Models: Revealing Hidden Safety Flaws in Diffusion-Based Text Generation","created":"2025-07-25","cited_by_count":26,"area":"lr"},{"id":"2305.11164","title":"Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study","created":"2023-05-18","cited_by_count":23,"area":null},{"id":"2306.08744","title":"High-performance deep spiking neural networks with 0.3 spikes per neuron","created":"2023-06-14","cited_by_count":22,"area":null},{"id":"2506.00551","title":"AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation","created":"2025-05-31","cited_by_count":21,"area":"agents"},{"id":"2510.01149","title":"ModernVBERT: Towards Smaller Visual Document Retrievers","created":"2025-10-01","cited_by_count":21,"area":"rag"},{"id":"2503.07675","title":"DynTaskMAS: A Dynamic Task Graph-driven Framework for Asynchronous and Parallel LLM-based Multi-Agent Systems","created":"2025-03-10","cited_by_count":20,"area":"agents"},{"id":"2205.05071","title":"Towards Climate Awareness in NLP Research","created":"2022-05-10","cited_by_count":19,"area":null},{"id":"2311.05842","title":"AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems","created":"2023-11-10","cited_by_count":19,"area":null},{"id":"2408.05235","title":"SLO-aware GPU Frequency Scaling for Energy Efficient LLM Inference Serving","created":"2024-08-05","cited_by_count":19,"area":"inference"},{"id":"2307.09988","title":"TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge","created":"2023-07-19","cited_by_count":18,"area":null},{"id":"2508.03772","title":"GTPO: Stabilizing Group Relative Policy Optimization via Gradient and Entropy Control","created":"2025-08-05","cited_by_count":18,"area":"lr"},{"id":"2502.11843","title":"Can LLM Agents Maintain a Persona in Discourse?","created":"2025-02-17","cited_by_count":15,"area":"agents"},{"id":"2505.14733","title":"The Energy Cost of Reasoning: Analyzing Energy Usage in LLMs with Test-time Compute","created":"2025-05-20","cited_by_count":15,"area":"lr"},{"id":"2310.02784","title":"MAD Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems","created":"2023-10-04","cited_by_count":14,"area":null},{"id":"2403.12900","title":"Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inference","created":"2024-03-19","cited_by_count":13,"area":null},{"id":"2411.07854","title":"Tucano: Advancing Neural Text Generation for Portuguese","created":"2024-11-12","cited_by_count":13,"area":null},{"id":"2506.07851","title":"Learning to Focus: Causal Attention Distillation via Gradient-Guided Token Pruning","created":"2025-06-09","cited_by_count":13,"area":"inference"},{"id":"2509.03986","title":"Promptception: How Sensitive Are Large Multimodal Models to Prompts?","created":"2025-09-04","cited_by_count":13,"area":"mllm"},{"id":"2210.10514","title":"The Future of Consumer Edge-AI Computing","created":"2022-10-19","cited_by_count":12,"area":null},{"id":"2309.02065","title":"Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI","created":"2023-09-05","cited_by_count":12,"area":null},{"id":"2405.05135","title":"Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks","created":"2024-04-24","cited_by_count":12,"area":null},{"id":"2501.01990","title":"Towards Sustainable Large Language Model Serving","created":"2024-12-31","cited_by_count":12,"area":null},{"id":"2302.06117","title":"The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment","created":"2023-02-13","cited_by_count":11,"area":null},{"id":"2305.15092","title":"FedZero: Leveraging Renewable Excess Energy in Federated Learning","created":"2023-05-24","cited_by_count":11,"area":null},{"id":"2311.10267","title":"Energy and Carbon Considerations of Fine-Tuning BERT","created":"2023-11-17","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":"2504.08231","title":"Out of Style: RAG's Fragility to Linguistic Variation","created":"2025-04-11","cited_by_count":11,"area":"rag"},{"id":"2303.06219","title":"The Carbon Emissions of Writing and Illustrating Are Lower for AI than for Humans","created":"2023-03-08","cited_by_count":10,"area":null},{"id":"2307.05494","title":"Towards Environmentally Equitable AI via Geographical Load Balancing","created":"2023-06-20","cited_by_count":10,"area":null},{"id":"2504.02181","title":"A Survey of Scaling in Large Language Model Reasoning","created":"2025-04-02","cited_by_count":10,"area":"lr"},{"id":"2211.05239","title":"RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure","created":"2022-11-09","cited_by_count":9,"area":null},{"id":"2308.12908","title":"POLCA: Power Oversubscription in LLM Cloud Providers","created":"2023-08-24","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":"2303.14604","title":"Green Federated Learning","created":"2023-03-26","cited_by_count":8,"area":null},{"id":"2307.08309","title":"LogPr\\'ecis: Unleashing Language Models for Automated Malicious Log Analysis","created":"2023-07-17","cited_by_count":8,"area":null},{"id":"2308.14929","title":"Maestro: Uncovering Low-Rank Structures via Trainable Decomposition","created":"2023-08-28","cited_by_count":8,"area":null},{"id":"2406.05303","title":"Beyond Efficiency: Scaling AI Sustainably","created":"2024-06-08","cited_by_count":8,"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":"2305.12544","title":"Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models","created":"2023-05-21","cited_by_count":7,"area":null},{"id":"2307.09701","title":"Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation","created":"2023-07-19","cited_by_count":7,"area":null},{"id":"2401.01851","title":"The Power of Training: How Different Neural Network Setups Influence the Energy Demand","created":"2024-01-03","cited_by_count":7,"area":null},{"id":"2405.02385","title":"Efficient Deep Learning with Decorrelated Backpropagation","created":"2024-05-03","cited_by_count":7,"area":null},{"id":"2411.13626","title":"Principles of Visual Tokens for Efficient Video Understanding","created":"2024-11-20","cited_by_count":7,"area":null},{"id":"2505.20112","title":"ERC-SVD: Error-Controlled SVD for Large Language Model Compression","created":"2025-05-26","cited_by_count":7,"area":null},{"id":"2312.10193","title":"Adaptive Computation Modules: Granular Conditional Computation For Efficient Inference","created":"2023-12-15","cited_by_count":6,"area":null},{"id":"2312.11985","title":"Climate Change from Large Language Models","created":"2023-12-19","cited_by_count":6,"area":null},{"id":"2312.13038","title":"AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting","created":"2023-12-20","cited_by_count":6,"area":null},{"id":"2405.11612","title":"Sociotechnical Implications of Generative Artificial Intelligence for Information Access","created":"2024-05-19","cited_by_count":6,"area":null},{"id":"2510.05307","title":"When Should Users Check? Modeling Confirmation Frequency in Multi-Step Agentic AI Tasks","created":"2025-10-06","cited_by_count":6,"area":"agents"},{"id":"2209.07912","title":"Algorithmic decision making methods for fair credit scoring","created":"2022-09-16","cited_by_count":5,"area":null},{"id":"2209.12816","title":"Fast-FNet: Accelerating Transformer Encoder Models via Efficient Fourier Layers","created":"2022-09-26","cited_by_count":5,"area":null},{"id":"2301.11581","title":"A Green(er) World for A.I","created":"2023-01-27","cited_by_count":5,"area":null},{"id":"2306.16900","title":"Surveying (Dis)Parities and Concerns of Compute Hungry NLP Research","created":"2023-06-29","cited_by_count":5,"area":null},{"id":"2401.03454","title":"Computational Argumentation-based Chatbots: a Survey","created":"2024-01-07","cited_by_count":5,"area":null},{"id":"2406.02648","title":"Exploring Effects of Hyperdimensional Vectors for Tsetlin Machines","created":"2024-06-04","cited_by_count":5,"area":null},{"id":"2501.08413","title":"Labeling Free-text Data using Language Model Ensembles","created":"2025-01-14","cited_by_count":5,"area":null},{"id":"2505.17122","title":"Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data?","created":"2025-05-21","cited_by_count":5,"area":"alignment"},{"id":"2506.17951","title":"A Comprehensive Graph Framework for Question Answering with Mode-Seeking Preference Alignment","created":"2025-06-22","cited_by_count":5,"area":"rag"},{"id":"2507.00004","title":"A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search","created":"2025-06-10","cited_by_count":5,"area":"lr"},{"id":"2207.00081","title":"Sustainable Computing -- Without the Hot Air","created":"2022-06-30","cited_by_count":4,"area":null},{"id":"2307.05520","title":"Estimating Deep Learning energy consumption based on model architecture and training environment","created":"2023-07-07","cited_by_count":4,"area":null},{"id":"2307.11434","title":"Batching for Green AI -- An Exploratory Study on Inference","created":"2023-07-21","cited_by_count":4,"area":null},{"id":"2406.14328","title":"Computing Within Limits: An Empirical Study of Energy Consumption in ML Training and Inference","created":"2024-06-20","cited_by_count":4,"area":null},{"id":"2409.18749","title":"TensorSocket: Shared Data Loading for Deep Learning Training","created":"2024-09-27","cited_by_count":4,"area":null},{"id":"2605.09278","title":"EquiMem: Calibrating Shared Memory in Multi-Agent Debate via Game-Theoretic Equilibrium","created":"2026-05-10","cited_by_count":4,"area":"agents"},{"id":"2209.04448","title":"Learning sparse auto-encoders for green AI image coding","created":"2022-09-09","cited_by_count":3,"area":null},{"id":"2210.10684","title":"Language Models Understand Us, Poorly","created":"2022-10-19","cited_by_count":3,"area":null},{"id":"2401.11512","title":"TERC: A Transfer Entropy Redundancy Criterion for State Variable Selection in Reinforcement Learning","created":"2024-01-21","cited_by_count":3,"area":"deeprl"},{"id":"2401.17733","title":"Towards Physical Plausibility in Neuroevolution Systems","created":"2024-01-31","cited_by_count":3,"area":null},{"id":"2404.05241","title":"LightFF: Lightweight Inference for Forward-Forward Algorithm","created":"2024-04-08","cited_by_count":3,"area":null},{"id":"2503.10427","title":"VisTW: Benchmarking Vision-Language Models for Traditional Chinese in Taiwan","created":"2025-03-13","cited_by_count":3,"area":"mllm"},{"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":"2605.08146","title":"VT-Bench: A Unified Benchmark for Visual-Tabular Multi-Modal Learning","created":"2026-05-03","cited_by_count":3,"area":null},{"id":"2306.13686","title":"Broadening the perspective for sustainable AI: Comprehensive sustainability criteria and indicators for AI systems","created":"2023-06-22","cited_by_count":2,"area":null},{"id":"2309.16976","title":"Benchmarking and In-depth Performance Study of Large Language Models on Habana Gaudi Processors","created":"2023-09-29","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}],"next_cursor":"100"}