Paper Citation Record · LEDGER

Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models

As of 9 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 3 inbound Pith citation observations for arXiv:2502.05610.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.05610 v2

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:41:32.543879Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:09:10.858236Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T17:57:20.957533Z

Reference resolution

51 of 51 outbound references displayed

verified exact2
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External citation measurements

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Outbound references

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This paper cites online" 'onlinestring :=.

Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models online" 'onlinestring :=

Reference 1

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models write newline

Reference 2

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Observation 2cd3fa8d-fd05-46fa-8d26-4ba391a18cd5 · outbound

This paper cites Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models.

Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

Reference 3

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Unresolved cited work

Reference 4

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Unresolved cited work

Reference 5

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Unresolved cited work

Reference 6

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Observation ea456413-525c-463b-b4fb-08605cf2e427 · outbound

This paper cites Towards Accurate and Reliable Energy Measurement of NLP Models.

Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Towards Accurate and Reliable Energy Measurement of NLP Models

Reference 7

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This paper cites Accelerating Large Language Model Decoding with Speculative Sampling.

Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Accelerating Large Language Model Decoding with Speculative Sampling

Reference 8

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This paper cites FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance.

Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance

Reference 9

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Unresolved cited work

Reference 10

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Observation cb14421e-76cf-4633-aca0-cbe6b3990aeb · outbound

This paper cites Compute and Energy Consumption Trends in Deep Learning Inference.

Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Compute and Energy Consumption Trends in Deep Learning Inference

Reference 11

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Unresolved cited work

Reference 12

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Unresolved cited work

Reference 13

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This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 14

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Reference 15

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Reference 16

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Unresolved cited work

Reference 17

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Unresolved cited work

Reference 18

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This paper cites Quantifying the Carbon Emissions of Machine Learning.

Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Quantifying the Carbon Emissions of Machine Learning

Reference 19

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Unresolved cited work

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inference

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Towards Quantifying the Carbon Emissions of Differentially Private Machine Learning

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Carbon Emissions and Large Neural Network Training

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Efficiently Scaling Transformer Inference

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters

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Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models Tree of Thoughts: Deliberate Problem Solving with Large Language Models

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Pith citing papers

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Brevity is the soul of sustainability: Characterizing LLM response lengths cites this paper.

Brevity is the soul of sustainability: Characterizing LLM response lengths Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models

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A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection cites this paper.

A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models

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SLM-Bench: A Comprehensive Benchmark of Small Language Models on Environmental Impacts--Extended Version cites this paper.

SLM-Bench: A Comprehensive Benchmark of Small Language Models on Environmental Impacts--Extended Version Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models

Reference 38

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