Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T14:23:01.914337Z
Paper Citation Record · LEDGER
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning
As of 17 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 2 inbound Pith citation observations for arXiv:2501.15398.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T04:33:03.169587Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-04T22:40:49.074122Z
Reference resolution
27 of 27 outbound references displayed
- verified exact0
- verified fuzzy22
- unresolved5
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 01bd2883-3af0-4158-9297-b3b54efe3732 · outbound
This paper cites Green artificial intelligence initiatives: Potentials and challenges.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Green artificial intelligence initiatives: Potentials and challenges
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.177206Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 009ff218-ddbb-4e03-ada1-7ee19c0a748c · outbound
This paper cites SciBERT: A pretrained language model for scientific text.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning SciBERT: A pretrained language model for scientific text
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.168248Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8b38de7f-6dd7-4aea-9d68-c3a8e7574aad · outbound
This paper cites Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.159411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f5b4c8fe-4b34-41f9-934f-6f387dbfd8fc · outbound
This paper cites How to estimate carbon footprint when training deep learning models? a guide and review.Environmental Research Communications, 5(11):115014, 2023.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning How to estimate carbon footprint when training deep learning models? a guide and review.Environmental Research Communications, 5(11):115014, 2023
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.150411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 696e1dc4-329c-43ee-8242-a0354e9b3597 · outbound
This paper cites Extracting highlights of scientific articles: A supervised summarization approach.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Extracting highlights of scientific articles: A supervised summarization approach
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.141260Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d51a4a54-103a-42e2-b93d-a778b94aa4a1 · outbound
This paper cites A supervised approach to extractive summarisation of scientific papers.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning A supervised approach to extractive summarisation of scientific papers
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.132192Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6ba27062-be4f-467e-94f6-ac0ba1a51eb2 · outbound
This paper cites Efficiency – data centers, 2025.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Efficiency – data centers, 2025
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.122297Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6f770ca7-24ca-4573-91da-ffd442aaa378 · outbound
This paper cites LORA: Low-rank adaptation of large language models.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning LORA: Low-rank adaptation of large language models
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.113617Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f1acde2c-8c40-4a16-98c4-4a375a745489 · outbound
This paper cites Global energy & CO 2 status report 2019, 2019.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Global energy & CO 2 status report 2019, 2019
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.104776Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 9c5c2dd7-5500-4fcd-ba7e-17f961741674 · outbound
This paper cites Green algorithms: quantifying the carbon footprint of computation.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Green algorithms: quantifying the carbon footprint of computation
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.095613Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7938ceb0-1ab7-4eb5-ba41-8ef7d35bf13e · outbound
This paper cites BART: Denoising sequence-to- sequence pre-training for natural language generation, translation, and comprehension.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning BART: Denoising sequence-to- sequence pre-training for natural language generation, translation, and comprehension
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.086626Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation b0a29030-cf25-433c-98c2-b7921f66d4a6 · outbound
This paper cites The automatic creation of literature abstracts.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning The automatic creation of literature abstracts
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.078284Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation ad883a3f-52f2-4d05-9a6c-8828db796270 · outbound
This paper cites Large Language Models: A Survey.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Large Language Models: A Survey
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:23:01.876069Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 63d965a0-2ec7-4a0a-887e-960f0f3d4446 · outbound
This paper cites Carbon Emissions and Large Neural Network Training.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Carbon Emissions and Large Neural Network Training
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:23:01.879646Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fa8b9477-f9e0-4a32-aa5d-e013ccac174b · outbound
This paper cites an unresolved cited work.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Unresolved cited work
Reference 15
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T14:23:02.070094Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a2e1eb5f-1a04-4e2d-88fd-4951e7cf3e43 · outbound
This paper cites An analysis of abstractive text summarization using pre-trained models.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning An analysis of abstractive text summarization using pre-trained models
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.062236Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation fadb5c2d-a9fa-492b-be7b-8027fcee3d38 · outbound
This paper cites Research highlight generation with ELMo contextual embeddings.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Research highlight generation with ELMo contextual embeddings
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.054062Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation c69dcdd2-1210-4dc6-8aa9-23ce5a4786cc · outbound
This paper cites Can pre-trained language models generate titles for research papers? In Proceedings of the International Conference on Asian Digital Libraries, pages 154–170.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Can pre-trained language models generate titles for research papers? In Proceedings of the International Conference on Asian Digital Libraries, pages 154–170
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.045242Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation dbe5ea64-f419-42b7-ad94-b64520bf66b5 · outbound
This paper cites Automatic generation of research highlights from scientific abstracts.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Automatic generation of research highlights from scientific abstracts
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.035778Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3554318f-2d53-4ad1-85c3-cc1769ad4c41 · outbound
This paper cites Generation of highlights from research papers using pointer-generator networks and SciBERT embeddings.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Generation of highlights from research papers using pointer-generator networks and SciBERT embeddings
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.025408Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 1c04a35b-5323-4639-bda1-76f836080519 · outbound
This paper cites Named entity recognition based automatic generation of research highlights.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Named entity recognition based automatic generation of research highlights
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.015399Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 4e3ada36-5a48-401b-8cc2-f01cb91ef873 · outbound
This paper cites Green AI.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Green AI
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:02.005531Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 5d871819-0a3d-4ad2-b561-89aa3ef5d176 · outbound
This paper cites See, Peter J.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning See, Peter J
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:01.995744Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8eda6388-c52d-4ea8-a0e9-b5176dbda573 · outbound
This paper cites Energy and policy considerations for deep learning in NLP.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Energy and policy considerations for deep learning in NLP
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:01.986041Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a021e88e-4b35-4f1b-9907-8358cadbdd87 · outbound
This paper cites LLaMA: Open and Efficient Foundation Language Models.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning LLaMA: Open and Efficient Foundation Language Models
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:23:01.908116Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8e506ace-cb67-4983-ab7d-888d175db78e · outbound
This paper cites Attention is all you need.Advances in Neural Information Processing Systems, 30, 2017.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning Attention is all you need.Advances in Neural Information Processing Systems, 30, 2017
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:23:01.911238Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f61dc1ca-8b93-48ee-a7a6-af86de8d44d3 · outbound
This paper cites A systematic review of green AI.
How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning A systematic review of green AI
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:23:01.968770Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
Observation df797af5-d785-4bd9-b090-12869ec2afb6 · inbound
Electricity Demand and Grid Impacts of AI Data Centers: Challenges and Prospects cites this paper.
Electricity Demand and Grid Impacts of AI Data Centers: Challenges and Prospects How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning
Reference 62
- Resolution
- verified exact
- local_arxiv, observed 2026-08-04T22:40:49.079614Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0b25c5bf-e63e-4d13-99e5-99f46429879c · inbound
F$^2$Agent: Financial Fusion of Agentic Intelligence for Multimodal Trading cites this paper.
F$^2$Agent: Financial Fusion of Agentic Intelligence for Multimodal Trading How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning
Reference 2015
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T04:33:03.169587Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.