Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-09T12:18:49.114898Z
Paper Citation Record · LEDGER
As of 9 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2502.02412.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-09T12:18:49.114898Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-15T16:37:16.968173Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
41 of 41 outbound references displayed
External citation measurements
1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
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AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Impacts of software and its engineering on the carbon footprint of ict,
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Observation 8dd9e4e7-a42a-4c55-8c71-93873d92d6a6 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Is software “green
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Observation 6d44d3e4-5101-4314-83d1-13b9c31af001 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code An empirical study of practitioners’ perspectives on green software engineering,
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Observation cf4352a7-d05e-4f38-a872-f6bf888ccad9 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code A comprehensive review of green computing: Past, present, and future research,
Reference 4
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Observation e3ca8ad8-4a19-4438-84c7-1ee7e14f98ba · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Future data center energy- conservation and emission-reduction technologies in the context of smart and low-carbon city construction,
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Observation 282e7461-c2d0-4f99-b775-c0f471e8a33a · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Ict sector electricity consumption and greenhouse gas emissions – 2020 outcome,
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Observation 7c950a4c-1b43-410a-a8e0-c64fe1f03cc0 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation
Reference 7
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Observation 4fc51840-9dab-403f-a842-10232263207a · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Green artificial intelligence initiatives: Potentials and challenges,
Reference 8
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Observation dc11fcc3-6990-4433-b6d5-739d36b0a4a5 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Ai-powered green cloud and data center,
Reference 9
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Observation 0cdfcdeb-d5b2-4646-a5cd-c3e458898872 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Trends in ai inference energy consumption: Beyond the performance-vs- parameter laws of deep learning,
Reference 10
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Observation 8f0bf52d-e505-4280-892b-cedbc0011273 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Estimating the carbon footprint of bloom, a 176b parameter language model,
Reference 11
Source-reported events for the cited work
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Observation 64856773-8fa5-46f3-b44b-cfeb6d7e39e5 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Reducing carbon footprint in ai: A framework for sustainable training of large language models,
Reference 12
Source-reported events for the cited work
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Observation 7cad6f4e-5ced-4788-be24-86b9f2c45265 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code A review of green artificial intelligence: Towards a more sustainable future,
Reference 13
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Observation 4cdfb38a-7e68-458f-9258-6f813b54639b · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Github repository: Energyefficiencyllmcode,
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Observation 9659cdd2-e317-4f71-85cd-c8c9e05bc0d4 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code A review on code generation with llms: Application and evaluation,
Reference 15
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Observation 80895743-4774-461b-ad1a-f9b1edf80b84 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Evaluating the quality of llm-generated explanations for logical errors in cs1 student programs,
Reference 16
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Observation 42b00ceb-9800-49ba-a75a-f10af7201def · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Evaluating Large Language Models Trained on Code
Reference 17
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Observation a3aef581-a744-49cc-97b1-031cd26a7fed · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code On sample-efficient code generation,
Reference 18
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Observation 51c2380b-61f2-41de-985b-77be395d0bbb · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Enhancing software development efficiency through ai- powered code generation,
Reference 19
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Observation 2d2e9bb3-44db-41ce-9c4c-87dbd5e221c1 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Assessing the quality of github copilot’s code generation,
Reference 20
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Observation 0ddb12fb-11be-4ace-89f9-c91f45389866 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code A performance study of llm-generated code on leetcode,
Reference 21
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Observation 406d7856-c4f2-4b9f-b811-512d5fe80b32 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Ranking programming languages by energy efficiency,
Reference 22
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Observation 822e8bfc-dac8-478b-8a0a-30098a4d8b37 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Mercury: A Code Efficiency Benchmark for Code Large Language Models
Reference 23
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Observation 62024d86-28e7-46f3-9ac5-a342f004b447 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code EffiBench: Benchmarking the Efficiency of Automatically Generated Code
Reference 24
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Observation 76c84be8-4dc3-46b6-a075-3e9d0a5a4693 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Codereval: A benchmark of pragmatic code generation with generative pre-trained models,
Reference 25
Source-reported events for the cited work
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Observation 5cc8448c-d6aa-491b-b7ed-bde54e8ab2ae · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Measuring Coding Challenge Competence With APPS
Reference 26
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Observation c281ff54-b71e-4d38-be5c-65d4d82bb5d0 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code ReCode: Robustness evaluation of code generation models,
Reference 27
Source-reported events for the cited work
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Observation 14adca8b-58d1-40e0-a177-07bc45b600ea · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning
Reference 28
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Observation 92af4e47-120b-4794-91bb-615aa660321b · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Evaluating Language Models for Efficient Code Generation
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AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Evaluating large language models in class-level code generation,
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Observation 2fe06cee-792d-4b49-a26d-47ed4a9fb628 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Piloting Copilot, Codex, and StarCoder2: Hot Temperature, Cold Prompts, or Black Magic?
Reference 31
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Observation 16eaa6a8-91a3-43f4-b302-91d94f74ce87 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code An empirical evaluation of github copilot’s code suggestions,
Reference 32
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AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Generation probabilities are not enough: Uncertainty highlighting in ai code completions,
Reference 33
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Observation 92f70fce-25fd-400b-ac3e-77b386fc8e10 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code van Solingen (Revision), V
Reference 34
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Observation cf811f66-ae3c-467c-be83-8cb3357c5dbd · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code On evaluating the efficiency of source code generated by llms,
Reference 35
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AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Intel® performance counter monitor - a better way to measure cpu
Reference 36
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Observation df1d2b21-9c38-4517-b091-27ff705fc316 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code RAPL in action: Experiences in using RAPL for power measurements,
Reference 37
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Observation f4d3c422-657f-4e2a-876d-07db6fd25ae2 · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Understanding energy behaviors of thread management constructs,
Reference 38
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AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Energy efficiency analysis of compiler optimizations on the spec cpu 2017 benchmark suite,
Reference 39
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Observation ceff4b4d-f363-48e9-947d-99ceac883cdb · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Available: https://api.semanticscholar.org/CorpusID: 234790100
Reference 2021
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Observation bb793cdf-3fd4-4bed-8b3a-fbdff12a2b5f · outbound
AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code Available: https://doi.org/10.1145/3597503.3623316
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Observation 96c31ea6-e5c9-49cc-90e1-b61f321409fc · inbound
An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code AI-Powered, But Power-Hungry? Energy Efficiency of LLM-Generated Code
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