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Paper Citation Record · LEDGER

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics

As of 7 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2507.03004.

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

pith.paper-citation-record.v1
2507.03004 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:59:05.807268Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

  • verified exact4
  • verified fuzzy13
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation aaec66fd-bc79-4ffc-8a35-33da17352c3a · outbound

This paper cites Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization

Reference 1

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source=pdf_text observed=2026-08-06T20:59:03.016155Z digest=sha256:38b303259d31e8701fc9a3e4b54bc79199ae05fe8804e0cb860b4fcd26275a63

Observation 572e5edd-f68a-48ee-b94d-02ff5eb4b76d · outbound

This paper cites Swad: Domain generalization by seeking flat minima.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Swad: Domain generalization by seeking flat minima

Reference 2

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source=pdf_text observed=2026-08-06T20:59:03.086726Z digest=sha256:95f52e3b7ce248785ce55598a3635caaf6ae0dde630a92d5c6e31e3ab232c8c4

Observation 38ce1e0c-ebb4-4d27-a93e-97999bd3ed73 · outbound

This paper cites Redpajama: an open dataset for training large language models, 2023.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Redpajama: an open dataset for training large language models, 2023

Reference 3

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source=pdf_text observed=2026-08-06T20:59:03.150293Z digest=sha256:8c69950b408d6d8856d4bd329e4a81ef6c87e508e980e0240ed9df10f5435129

Observation edd93686-42b9-412c-afe2-eff4b3ec2884 · outbound

This paper cites ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning

Reference 4

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source=pdf_text observed=2026-08-06T20:59:03.238055Z digest=sha256:80dabc93042be71e3205d18592ad85af75fbde84019baafa659af9bebd4c0f6a

Observation 47657797-cbe2-4693-8e1c-828c9bc605e7 · outbound

This paper cites fingpt-fiqa_qa.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics fingpt-fiqa_qa

Reference 5

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:59:03.271090Z digest=sha256:b2f9f935cafef7958ecdf47c99a43bfe49dbef7ce5c989a7b82efe638a6e2864

Observation 233ec159-5fac-4c5a-bda5-1ed459a0591a · outbound

This paper cites Arcee's MergeKit: A Toolkit for Merging Large Language Models.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Arcee's MergeKit: A Toolkit for Merging Large Language Models

Reference 6

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source=pdf_text observed=2026-08-06T20:59:03.405349Z digest=sha256:df72c1ad7790d88a3290486824cf5580cde0f6c21019f54f35d1004184cf6100

Observation 06f926cf-8a87-4a15-b003-00af713ae37e · outbound

This paper cites Stochastic Weight Averaging in Parallel: Large-Batch Training that Generalizes Well.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Stochastic Weight Averaging in Parallel: Large-Batch Training that Generalizes Well

Reference 7

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source=pdf_text observed=2026-08-06T20:59:03.457350Z digest=sha256:8f16914f28bae1e8cd028c44701012303c5ee7766acd77c85263a6b0164f5c17

Observation f384dfab-44f5-43ee-bdaf-54de593205b1 · outbound

This paper cites MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data

Reference 8

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source=pdf_text observed=2026-08-06T20:59:03.537575Z digest=sha256:6ff9dfecf6bde5eb6aac2df6df78db37041ae3404475a9ebc099340cc6043859

Observation 5c03d426-077e-46c1-87c6-5f6ae6c65225 · outbound

This paper cites Aligning ai with shared human values.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Aligning ai with shared human values

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:59:03.614700Z digest=sha256:bb57a8b5570080ae41662ebff40eb3541dc52c46101470b252b4a04713fdafed

Observation 3e8fa007-e055-4d06-b35d-9410b90bd1fe · outbound

This paper cites Measuring massive multitask language understanding.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Measuring massive multitask language understanding

Reference 10

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source=pdf_text observed=2026-08-06T20:59:03.688113Z digest=sha256:99d8b796a3083a07948e60212c31580b1f64a3dcdb45574df89ef4b39e8a69bf

Observation dea18d27-d4d6-4c69-8d0c-b4eddf2784f8 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics LoRA: Low-Rank Adaptation of Large Language Models

Reference 11

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Observation adef72ab-8162-40df-bd11-f5bdd8788cf8 · outbound

This paper cites Editing Models with Task Arithmetic.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Editing Models with Task Arithmetic

Reference 12

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source=pdf_text observed=2026-08-06T20:59:03.793097Z digest=sha256:13e33b926ef930c251a84a4d2cb6da59aef686e1179eeb2311a799a7819e649f

Observation 29e40bf6-489f-46b2-aba5-bfaa11642f77 · outbound

This paper cites Editing models with task arithmetic.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Editing models with task arithmetic

Reference 13

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source=pdf_text observed=2026-08-06T20:59:03.882507Z digest=sha256:d2e7a89690cbcb9346bfc181894490d0471df97d1167969aef9a02ed7be8ad3c

Observation 44e194b9-90c5-4e62-84b6-13799926e0df · outbound

This paper cites Averaging Weights Leads to Wider Optima and Better Generalization.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Averaging Weights Leads to Wider Optima and Better Generalization

Reference 14

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source=pdf_text observed=2026-08-06T20:59:03.979947Z digest=sha256:9275fc89b766d2517213a9d68efefe41eb7e9b0df18b1d30bd9214ac7c50872c

Observation 23e8ea93-bcdb-457e-9987-41bcc420b476 · outbound

This paper cites Mistral 7B.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Mistral 7B

Reference 15

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source=pdf_text observed=2026-08-06T20:59:04.054223Z digest=sha256:1b50ce3c07f7821703a75f2877bb65f05d862aa7fcdb893d7db5c2d2b5b4233c

Observation d1d4183e-6672-4d6f-b8fb-04e3c61b6317 · outbound

This paper cites What disease does this patient have? a large-scale open domain question answering dataset from medical exams.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics What disease does this patient have? a large-scale open domain question answering dataset from medical exams

Reference 16

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source=pdf_text observed=2026-08-06T20:59:04.112753Z digest=sha256:f3489a8a0bd3665b3d50528571ede5e5d0ac8589c63d6a64c5e00149502debce

Observation 7cd79f5e-271d-47ac-9905-102d29b8a2ed · outbound

This paper cites PubMedQA: A Dataset for Biomedical Research Question Answering.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics PubMedQA: A Dataset for Biomedical Research Question Answering

Reference 17

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Observation 73b9f224-2889-49f1-9818-9853e3743ba2 · outbound

This paper cites Dataless Knowledge Fusion by Merging Weights of Language Models.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Dataless Knowledge Fusion by Merging Weights of Language Models

Reference 18

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source=pdf_text observed=2026-08-06T20:59:04.284153Z digest=sha256:e2e9d1e751a651d08e5f66295777ca4aa50c20d391d249e246616efe011148d4

Observation c14d7b81-4133-40c1-8ea1-78ef07c2bf85 · outbound

This paper cites Kingma and Jimmy Ba.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Kingma and Jimmy Ba

Reference 19

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source=pdf_text observed=2026-08-06T20:59:04.344075Z digest=sha256:66ec0045111c05a1dd8321bf02a4cf866b4806e221d45348cefa5594b10284bb

Observation b72e4507-2984-49d4-992e-2f1c6e57a487 · outbound

This paper cites Understanding black-box predictions via influence functions.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Understanding black-box predictions via influence functions

Reference 20

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source=pdf_text observed=2026-08-06T20:59:04.413299Z digest=sha256:75ba728568924896bfe61c07274d93def39082fc4d785da48b277ecc8261c3b5

Observation 7d20b216-b0f3-4f58-942f-a5c4f2c97ed0 · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Federated Learning: Strategies for Improving Communication Efficiency

Reference 21

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source=pdf_text observed=2026-08-06T20:59:04.487530Z digest=sha256:c8e6f4ee624d7ce77fba2a113632b0a1ad12cae168cca83c0355d04a0be8d3d3

Observation e2ae71c0-b837-4dbb-9ef4-62d50e9e3ee5 · outbound

This paper cites DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models

Reference 22

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source=pdf_text observed=2026-08-06T20:59:04.543992Z digest=sha256:329a79b38ab8a0a2cb088da67d490fd1e8fe54bba578e9b39c3cb8223b58271f

Observation d76afe2c-0be6-4305-8abe-91a0e68ffbdc · outbound

This paper cites Deduplicating Training Data Makes Language Models Better.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Deduplicating Training Data Makes Language Models Better

Reference 23

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source=pdf_text observed=2026-08-06T20:59:04.607253Z digest=sha256:a875d00e7adffccd150f541e5b0a0296a8b4996be3251b4daaae23664e029fa4

Observation 24e3a7d0-fe10-400a-8c73-3d34e3d9eb7e · outbound

This paper cites Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models

Reference 24

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source=pdf_text observed=2026-08-06T20:59:04.662169Z digest=sha256:1a4946865ac4007245ae373a55458111f8d9ed63012cd6baae71c053182f61f2

Observation 2ca8c4a2-3841-4b2c-9632-0b03d8b467b3 · outbound

This paper cites From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 25

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source=pdf_text observed=2026-08-06T20:59:04.732253Z digest=sha256:221ac9bc87fcd7a9353a4793fb8b29a426c2e22f04ae74d727f43b49a70164a5

Observation 9edf96f8-4fdc-4348-9b18-af5b230dce42 · outbound

This paper cites Convergence analysis of two-layer neural networks with relu activation.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Convergence analysis of two-layer neural networks with relu activation

Reference 26

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:59:04.810874Z digest=sha256:e665d2d663e1f2dd4b35efbf5d34f6246ad198eec2163b8af3cbecb031c8c34c

Observation b5f36549-3092-4a43-b488-8c3fef8ff3f0 · outbound

This paper cites Decoupled weight decay regularization.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Decoupled weight decay regularization

Reference 27

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:59:04.859470Z digest=sha256:00d3d0fdb7d20bfaa448a3f222e6775894a867ed9fc371270b4c5200d2ed8ce7

Observation a22bc54d-1f72-4a4d-b984-30404b02b74c · outbound

This paper cites Peft: State-of-the-art parameter-efficient fine-tuning methods, 2022.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Peft: State-of-the-art parameter-efficient fine-tuning methods, 2022

Reference 28

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source=pdf_text observed=2026-08-06T20:59:04.898979Z digest=sha256:c4c9f79dc340f6b7db2b30eb638f322152f308bf4324acfa8b70631eb9663f71

Observation da38bd29-33fd-4215-a67d-096ed2cb5e62 · outbound

This paper cites Mondschein and Cosimo Monda.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Mondschein and Cosimo Monda

Reference 29

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source=pdf_text observed=2026-08-06T20:59:04.956644Z digest=sha256:1b7dad99bcabde45b96e77d0024b5c1afecc43693c03a345c1633b9387d0bd01

Observation 2f931eb1-dc06-4d8c-ba9b-afe80ae66a1e · outbound

This paper cites Scaling Data-Constrained Language Models.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Scaling Data-Constrained Language Models

Reference 30

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Observation 44ae9e10-007c-4325-b5be-4a6d15ed37d9 · outbound

This paper cites Gpt-4 technical report, 2023.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Gpt-4 technical report, 2023

Reference 31

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source=pdf_text observed=2026-08-06T20:59:05.049128Z digest=sha256:5c593f5aebb9b8eec9e9e68d9894d4247b53cc841654e6c4a1220593bc7c55a3

Observation 58927896-5d8a-4e21-b1ba-ebde90c9ca09 · outbound

This paper cites Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering

Reference 32

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source=pdf_text observed=2026-08-06T20:59:05.120120Z digest=sha256:4b518570fc0bcf8760850168c9f3e1ae64c6e4858770754cc787ac18ad2f5b11

Observation cc517d00-a1ab-4f66-9bd0-eb11672d3170 · outbound

This paper cites Pytorch: An imperative style, high- performance deep learning library.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Pytorch: An imperative style, high- performance deep learning library

Reference 33

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source=pdf_text observed=2026-08-06T20:59:05.167550Z digest=sha256:af56ce66e4de4b76a26aee25816a938a2d26e97184cd5b12d392a348629c819f

Observation d8a339a3-ce17-44bd-8d4d-bc94437fe42b · outbound

This paper cites Towards building multilingual language model for medicine, 2024.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Towards building multilingual language model for medicine, 2024

Reference 34

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source=pdf_text observed=2026-08-06T20:59:05.214502Z digest=sha256:99eb0bc926373d8771d5fa965f003b08a8162f4d7e7c69aa1144da433c09a9a1

Observation 1a4ebf0b-0241-4156-a348-ae44be7fefca · outbound

This paper cites Smith, and Yejin Choi.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Smith, and Yejin Choi

Reference 35

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source=pdf_text observed=2026-08-06T20:59:05.278207Z digest=sha256:f599b953d03267c7bc5c57a509ea5c79cd4c7100224520b5ad4ac65e17a5c5e9

Observation d85f393b-0f7e-4acb-ad6b-b691dc3c5779 · outbound

This paper cites Luck Matters: Understanding Training Dynamics of Deep ReLU Networks.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Luck Matters: Understanding Training Dynamics of Deep ReLU Networks

Reference 36

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local_arxiv, observed 2026-08-06T20:59:06.219462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:59:05.333594Z digest=sha256:d17bffeb52e28ccb363e2b6a091b3ed5a722b1470bff63a95bdd5ab5b563c0a1

Observation 03cd30a0-72e3-4df6-a945-3b401adc160d · outbound

This paper cites Llama: Open and efficient foundation language models, 2023.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Llama: Open and efficient foundation language models, 2023

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T20:59:05.393072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:05.393072Z digest=sha256:d3ac74a0e6aaaa5a8f799fc737932a0739b5e43f48f6e289af728f974191187d

Observation 4cb854e7-f732-4c3a-a0da-7d8cf00fcdae · outbound

This paper cites Llama 2: Open foundation and fine-tuned chat models, 2023.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Llama 2: Open foundation and fine-tuned chat models, 2023

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T20:59:05.458271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:05.458271Z digest=sha256:d3ebef577375f2def884a4f321873920d95038e8104fe7a9dc8b7c053d74aa92

Observation 80e75d6c-c521-41fd-bf54-d7bd2567e041 · outbound

This paper cites Wilson, James L.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Wilson, James L

Reference 39

Resolution
verified exact
doi, observed 2026-08-06T20:59:05.908802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:59:05.524371Z digest=sha256:ead915fd065f71ca4e149229078c8eff82c5e59c95807871b36a76cb6bc95910

Observation 5bd36964-1ed4-4161-bd38-27c029102767 · outbound

This paper cites Transformers: State-of-the-art natural language processing.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Transformers: State-of-the-art natural language processing

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-06T20:59:05.584862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:05.584862Z digest=sha256:d458e7a7896c7946e2704dfa7b31d45120a1076bfd8d2b95d71f429c66a5c795

Observation 50609034-247e-4c41-bbc3-8f3ca6128a6a · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:59:06.854254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:59:05.649932Z digest=sha256:d1a69196830868283aacd3045058126b209474975cbec767298e83ad0bbacbcd

Observation 7b749274-5670-49c3-81f0-cc4450a47a81 · outbound

This paper cites PMC-LLaMA: Towards Building Open-source Language Models for Medicine.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics PMC-LLaMA: Towards Building Open-source Language Models for Medicine

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-06T20:59:05.693572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:05.693572Z digest=sha256:2b1f875b1b91477d7c5910f57cef2cffb1a7029895ae334df5665c71470f05e6

Observation 3fe58433-ced4-4581-9302-70435df37255 · outbound

This paper cites TIES-merging: Resolving interference when merging models.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics TIES-merging: Resolving interference when merging models

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:59:06.600515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:59:05.750506Z digest=sha256:d841df50d441f4dae9ab4a64faefe82cb77578a61cc39c28b05a79611d4b7174

Observation 3f06e261-7424-493d-9280-ef4695199cc2 · outbound

This paper cites surprised.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics surprised

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-06T20:59:05.807268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:59:05.807268Z digest=sha256:fd11da0164e3d04c7a28079a9703f3951f1f940c7aa74bf9921da69b92d4c584

Observation 3baf143b-81a2-464f-9ca5-9e4e3ba88dbb · outbound

This paper cites an unresolved cited work.

CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics Unresolved cited work

Reference 2023

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:59:08.676193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:59:03.350941Z digest=sha256:9801d53fbcdf80e7d2b48ce9ed8cd5d1a7cbe0da55a1f2f311bb67f674496c81

Pith citing papers

No inbound Pith citation observations are available.