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Paper Citation Record · LEDGER

Towards Efficient and Effective Alignment of Large Language Models

As of 20 August 2026, this Paper Citation Record lists 100 of 240 outbound references and 0 inbound Pith citation observations for arXiv:2506.09329.

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

pith.paper-citation-record.v1
2506.09329 v1

Coverage vector

measured 100 of 240 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:55:42.739229Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

100 of 240 outbound references displayed

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  • verified fuzzy0
  • unresolved99
  • parse uncertain0
  • malformed identifier0
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Outbound references

Observation 2984a716-55b6-4c05-b1c8-2b424716993a · outbound

This paper cites Explanations for commonsenseqa: New dataset and models.

Towards Efficient and Effective Alignment of Large Language Models Explanations for commonsenseqa: New dataset and models

Reference 1

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Observation 3f267181-8541-4f56-8cc2-a05b3561a99d · outbound

This paper cites Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models.

Towards Efficient and Effective Alignment of Large Language Models Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models

Reference 2

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Observation db1cb79b-10f6-44c7-8f30-7c5326d99938 · outbound

This paper cites arxiv dataset, 2023.

Towards Efficient and Effective Alignment of Large Language Models arxiv dataset, 2023

Reference 3

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Observation cb4f1d21-4f1b-402d-8515-1dec66a8baf6 · outbound

This paper cites A General Language Assistant as a Laboratory for Alignment.

Towards Efficient and Effective Alignment of Large Language Models A General Language Assistant as a Laboratory for Alignment

Reference 4

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Observation f7fd65e2-7744-48f4-8cae-8451897ae442 · outbound

This paper cites Program Synthesis with Large Language Models.

Towards Efficient and Effective Alignment of Large Language Models Program Synthesis with Large Language Models

Reference 5

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Observation 2a2f7029-2d82-4d25-aec4-2ffc1fea85c2 · outbound

This paper cites A general theoretical paradigm to understand learning from human preferences.

Towards Efficient and Effective Alignment of Large Language Models A general theoretical paradigm to understand learning from human preferences

Reference 6

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Observation 6ddee455-f96b-4ce2-bf8e-d8d01ce24816 · outbound

This paper cites an unresolved cited work.

Towards Efficient and Effective Alignment of Large Language Models Unresolved cited work

Reference 7

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Observation ae4a5deb-b684-4c43-9b55-2da9714a0fc7 · outbound

This paper cites Qwen Technical Report.

Towards Efficient and Effective Alignment of Large Language Models Qwen Technical Report

Reference 8

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Observation 472e8260-1600-4b07-bc77-c0c6e79bd3a3 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Towards Efficient and Effective Alignment of Large Language Models Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 9

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Observation 9141b438-901d-4072-85d8-052d26f42ca4 · outbound

This paper cites Constitutional ai: Harmlessness from ai feedback.

Towards Efficient and Effective Alignment of Large Language Models Constitutional ai: Harmlessness from ai feedback

Reference 10

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Observation bc625a8d-77c2-4461-a291-d2866151238f · outbound

This paper cites Baichuan 2: Open Large-scale Language Models.

Towards Efficient and Effective Alignment of Large Language Models Baichuan 2: Open Large-scale Language Models

Reference 11

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Observation a4795861-b5bd-4e17-ac48-cd90fe38235d · outbound

This paper cites PIQA: reasoning about physical commonsense in natural language.

Towards Efficient and Effective Alignment of Large Language Models PIQA: reasoning about physical commonsense in natural language

Reference 12

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Observation f2613a1f-022c-4fcb-ac75-997afd90fee9 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Towards Efficient and Effective Alignment of Large Language Models On the Opportunities and Risks of Foundation Models

Reference 13

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Observation cf7bd0d9-897a-407f-8a08-5f12ee0be21f · outbound

This paper cites Rank analysis of incomplete block designs: I.

Towards Efficient and Effective Alignment of Large Language Models Rank analysis of incomplete block designs: I

Reference 14

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Observation 243e65c4-9ddc-4074-a351-1794a3c2c60f · outbound

This paper cites On the resemblance and containment of documents.

Towards Efficient and Effective Alignment of Large Language Models On the resemblance and containment of documents

Reference 15

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Observation 9cb5a7d5-bc30-470d-a784-98838ae56b84 · outbound

This paper cites Language models are few-shot learners.

Towards Efficient and Effective Alignment of Large Language Models Language models are few-shot learners

Reference 16

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Observation ca627d7f-b4dc-44e0-b650-d68c428947e6 · outbound

This paper cites Data Diversity Matters for Robust Instruction Tuning.

Towards Efficient and Effective Alignment of Large Language Models Data Diversity Matters for Robust Instruction Tuning

Reference 17

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Observation 6833eeb0-05f9-4dac-bc72-f0be0760cb3f · outbound

This paper cites Drlc: Reinforcement learning with dense rewards from llm critic.

Towards Efficient and Effective Alignment of Large Language Models Drlc: Reinforcement learning with dense rewards from llm critic

Reference 18

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Observation dcf1a6d7-877f-4d33-8226-a98385e3554e · outbound

This paper cites Instruction Mining: Instruction Data Selection for Tuning Large Language Models.

Towards Efficient and Effective Alignment of Large Language Models Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 19

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Observation 48a84e31-6eba-420c-a629-95694fd796b3 · outbound

This paper cites Wit3: Web inventory of transcribed and translated talks.

Towards Efficient and Effective Alignment of Large Language Models Wit3: Web inventory of transcribed and translated talks

Reference 20

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Observation 86d7480e-1b5e-40eb-ac40-e92c2671b1f0 · outbound

This paper cites Dense reward for free in reinforcement learning from human feedback.

Towards Efficient and Effective Alignment of Large Language Models Dense reward for free in reinforcement learning from human feedback

Reference 21

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Observation f5508cc0-8a67-49c5-9e72-5f3b9356a530 · outbound

This paper cites Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning.

Towards Efficient and Effective Alignment of Large Language Models Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 22

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Observation c2bc87fc-1d24-4014-87b0-f5b109d3af90 · outbound

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Towards Efficient and Effective Alignment of Large Language Models Controllable Text Generation with Language Constraints

Reference 23

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Observation 4fc93f12-58ed-420f-af94-e0141db37032 · outbound

This paper cites AlpaGasus: Training A Better Alpaca with Fewer Data.

Towards Efficient and Effective Alignment of Large Language Models AlpaGasus: Training A Better Alpaca with Fewer Data

Reference 24

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Observation e8374c56-37a3-4279-ad72-ec912cf5ccd9 · outbound

This paper cites an unresolved cited work.

Towards Efficient and Effective Alignment of Large Language Models Unresolved cited work

Reference 25

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Observation 3a881804-68d0-4530-bfb3-cc9bd2359f82 · outbound

This paper cites DoG- instruct: Towards premium instruction-tuning data via text-grounded instruction wrapping.

Towards Efficient and Effective Alignment of Large Language Models DoG- instruct: Towards premium instruction-tuning data via text-grounded instruction wrapping

Reference 26

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Observation 2a12aa9c-f84a-4aa2-9cd0-4835b5f0dbaa · outbound

This paper cites Improving large language models via fine-grained 111 reinforcement learning with minimum editing constraint.

Towards Efficient and Effective Alignment of Large Language Models Improving large language models via fine-grained 111 reinforcement learning with minimum editing constraint

Reference 27

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Observation 4d6f2caf-d786-4d3a-81c4-dba3f2d71162 · outbound

This paper cites Low-redundant optimization for large language model alignment.

Towards Efficient and Effective Alignment of Large Language Models Low-redundant optimization for large language model alignment

Reference 28

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Observation 80803c46-36c0-48d6-889f-552a222edb19 · outbound

This paper cites Replacing Language Model for Style Transfer.

Towards Efficient and Effective Alignment of Large Language Models Replacing Language Model for Style Transfer

Reference 29

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Observation eb703fef-2a92-40c8-9583-3552aeafc88b · outbound

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Towards Efficient and Effective Alignment of Large Language Models Unresolved cited work

Reference 30

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Observation 7b778db6-1805-49c2-997e-dad6126a2f32 · outbound

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Towards Efficient and Effective Alignment of Large Language Models Gonzalez, Ion Stoica, and Eric P

Reference 31

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Observation 311459ef-77b8-4637-859b-a47450733117 · outbound

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Towards Efficient and Effective Alignment of Large Language Models Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 32

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Observation bae9199e-0e62-4fbf-aadf-6df4203faff9 · outbound

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Towards Efficient and Effective Alignment of Large Language Models Unresolved cited work

Reference 33

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Observation e0423424-6cce-4c0b-801a-cbe6ae3f682c · outbound

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Towards Efficient and Effective Alignment of Large Language Models Christiano, Jan Leike, Tom B

Reference 34

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Observation 29a15694-57eb-46dc-b94e-a18d5928a149 · outbound

This paper cites All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text.

Towards Efficient and Effective Alignment of Large Language Models All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text

Reference 35

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Observation d4a9d89c-da52-47f0-a837-41137e909365 · outbound

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Towards Efficient and Effective Alignment of Large Language Models Think you have solved question answering? try arc, the ai2 reasoning challenge, 2018

Reference 36

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Observation 21c9fbf6-ba16-4326-9e2f-0c82a5569734 · outbound

This paper cites The future landscape of large language models in medicine.

Towards Efficient and Effective Alignment of Large Language Models The future landscape of large language models in medicine

Reference 37

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Observation ef90d198-6b0c-4258-a85e-cdeaa7796aa6 · outbound

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Towards Efficient and Effective Alignment of Large Language Models Training Verifiers to Solve Math Word Problems

Reference 38

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Observation 5da014c8-c844-4ace-b009-5c6e0c6f2402 · outbound

This paper cites Evaluating the Ripple Effects of Knowledge Editing in Language Models.

Towards Efficient and Effective Alignment of Large Language Models Evaluating the Ripple Effects of Knowledge Editing in Language Models

Reference 39

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Observation 56000ff9-8ca0-450d-9a6f-ce458accef52 · outbound

This paper cites Redpajama: An open source recipe to reproduce llama training dataset, 2023.

Towards Efficient and Effective Alignment of Large Language Models Redpajama: An open source recipe to reproduce llama training dataset, 2023

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source=pdf_text observed=2026-08-07T04:55:36.715510Z digest=sha256:629cd0d90546cbdffef42ecc7ed810f33f41af9c1713d2b742587fc917f2dd7d

Observation f103fa79-936b-48a6-abe1-a63c7af4b37c · outbound

This paper cites Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023.

Towards Efficient and Effective Alignment of Large Language Models Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023

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source=pdf_text observed=2026-08-07T04:55:36.803603Z digest=sha256:fd68ab508ad51a8aec4dd4e200b296cd259318f72995417863dc34858c199565

Observation 90f2194c-0d28-45a6-b9dc-84946743b6f0 · outbound

This paper cites Opencompass: A universal evaluation platform for foundation models.

Towards Efficient and Effective Alignment of Large Language Models Opencompass: A universal evaluation platform for foundation models

Reference 42

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source=pdf_text observed=2026-08-07T04:55:36.895228Z digest=sha256:322e35377ecc1ef7ca9ea0410efdcaa869a8624e0dfeb0e572680901f88cfbd2

Observation ed021ca8-dc4c-448c-97f5-7cc5f5219fe0 · outbound

This paper cites Ultrafeedback: Boosting language models with high-quality feedback.

Towards Efficient and Effective Alignment of Large Language Models Ultrafeedback: Boosting language models with high-quality feedback

Reference 43

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source=pdf_text observed=2026-08-07T04:55:37.011778Z digest=sha256:cade599430ae898ecaef13526d4e9e7af195ad090db440e24e82db339881c94a

Observation 06bbaec5-b94b-4928-b947-72112cd1f1ff · outbound

This paper cites Knowl- edge neurons in pretrained transformers.

Towards Efficient and Effective Alignment of Large Language Models Knowl- edge neurons in pretrained transformers

Reference 44

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source=pdf_text observed=2026-08-07T04:55:37.123213Z digest=sha256:a9a56f195ec8898ba1c73b28ac6bc5bbc4be9e746594ca5023e8bd385b6f5497

Observation 285a82bf-21c0-4aa3-9128-acd15a55a0ee · outbound

This paper cites Editing factual knowledge in language models.

Towards Efficient and Effective Alignment of Large Language Models Editing factual knowledge in language models

Reference 45

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source=pdf_text observed=2026-08-07T04:55:37.253184Z digest=sha256:f33ca3c165a0d70a68f0e8a06ab1f1bc6e9890207b2daf6352be481cf406bba9

Observation 1f7ab59e-699c-43a4-a443-7ed6b901c578 · outbound

This paper cites Bert: Pre- training of deep bidirectional transformers for language understanding.

Towards Efficient and Effective Alignment of Large Language Models Bert: Pre- training of deep bidirectional transformers for language understanding

Reference 46

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source=pdf_text observed=2026-08-07T04:55:37.420192Z digest=sha256:2240f461fc361e812f34032de233bc055c2ae495da17df54e887121062354d51

Observation d2bb9c16-8a0e-46ed-80da-cfd566084634 · outbound

This paper cites Enhancing chat language models by scaling high-quality instructional conversations.

Towards Efficient and Effective Alignment of Large Language Models Enhancing chat language models by scaling high-quality instructional conversations

Reference 47

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source=pdf_text observed=2026-08-07T04:55:37.579671Z digest=sha256:9f1f2ead413429900f3b6e91813638dedfb0518d5290d357d36373de94aa0d9f

Observation 0137709e-846f-4ca1-b11d-d3229c43b3ec · outbound

This paper cites A Survey on In-context Learning.

Towards Efficient and Effective Alignment of Large Language Models A Survey on In-context Learning

Reference 48

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source=pdf_text observed=2026-08-07T04:55:37.750155Z digest=sha256:5350767e8d2345ac16e497bcf61912ef760dbf95def8da9c9c92a08c1226b168

Observation 1eeede9d-3cf9-4718-b778-117133eeae85 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Towards Efficient and Effective Alignment of Large Language Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 49

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source=pdf_text observed=2026-08-07T04:55:37.923690Z digest=sha256:e317ab98706f3635111d4709f295574d5f55add331fb633748ff8f321c932a6c

Observation 3d846989-825f-4387-9fb5-8017ae6e716c · outbound

This paper cites Glm: General language model pretraining with autoregressive blank infilling.

Towards Efficient and Effective Alignment of Large Language Models Glm: General language model pretraining with autoregressive blank infilling

Reference 50

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source=pdf_text observed=2026-08-07T04:55:38.091691Z digest=sha256:7482f64b64f2b38ef4769526c9adf6b9f4bc9790fc9f6f822ad96d53fd2c7936

Observation 94750fad-64cb-41c8-a149-6fe7389653da · outbound

This paper cites The llama 3 herd of models.

Towards Efficient and Effective Alignment of Large Language Models The llama 3 herd of models

Reference 51

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source=pdf_text observed=2026-08-07T04:55:38.245201Z digest=sha256:3590e311049b830220ff1b7f9f18cb730706d3d0f04f895f0c536d0ce33fc8af

Observation 88326315-d97e-440e-bab5-8670052f5ef2 · outbound

This paper cites Length- controlled alpacaeval: A simple way to debias automatic evaluators.

Towards Efficient and Effective Alignment of Large Language Models Length- controlled alpacaeval: A simple way to debias automatic evaluators

Reference 52

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source=pdf_text observed=2026-08-07T04:55:38.319172Z digest=sha256:c08dd5aaf95669cda4d6528006c1c3c849a4997033e094d91bd30ffd4506e5f5

Observation c0c4a996-b02b-436b-b6bf-e7557988e64a · outbound

This paper cites Fact-checking the output of large language models via token-level uncertainty quantification.

Towards Efficient and Effective Alignment of Large Language Models Fact-checking the output of large language models via token-level uncertainty quantification

Reference 53

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source=pdf_text observed=2026-08-07T04:55:38.532163Z digest=sha256:5d18343a363c654cf810e3a5454b378da5879260f729956302779f8f35b137af

Observation 75ea9cfe-0741-4625-847a-1ea5d49c56ba · outbound

This paper cites Hierarchical neural story generation.

Towards Efficient and Effective Alignment of Large Language Models Hierarchical neural story generation

Reference 54

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source=pdf_text observed=2026-08-07T04:55:38.690711Z digest=sha256:8ce8e588a49090bd6b9ccf663bfd19fe35fc519c9d5a571ed07a555b996889c9

Observation 99594ee3-0f6e-4faa-b7e4-7a21323074c8 · outbound

This paper cites Data-Free Adversarial Distillation.

Towards Efficient and Effective Alignment of Large Language Models Data-Free Adversarial Distillation

Reference 55

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source=pdf_text observed=2026-08-07T04:55:38.892082Z digest=sha256:ea52b85984b754b0c78e5db0eb013e8482d53b8fb087bb303e566ca39e942c3f

Observation 1527546f-9b1d-4030-ac77-7fc2bc4d7c9f · outbound

This paper cites Gptscore: Evaluate as you desire.

Towards Efficient and Effective Alignment of Large Language Models Gptscore: Evaluate as you desire

Reference 56

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source=pdf_text observed=2026-08-07T04:55:39.059708Z digest=sha256:7e63d75abf6d8eca5ac77d775fb9d1b8217134e0847ad0740e195c2e65478ad5

Observation dc021117-b897-4423-bbb1-ab96af9c2935 · outbound

This paper cites Preference learning and ranking by pairwise comparison.

Towards Efficient and Effective Alignment of Large Language Models Preference learning and ranking by pairwise comparison

Reference 57

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source=pdf_text observed=2026-08-07T04:55:39.136611Z digest=sha256:2b7392c4dde6c3204b356ceef859c5760ce12cdbe5cdd003e01c7e0b566ecbeb

Observation 806fc54d-e1b9-4b59-9b4b-40a701927a5f · outbound

This paper cites Scaling Synthetic Data Creation with 1,000,000,000 Personas.

Towards Efficient and Effective Alignment of Large Language Models Scaling Synthetic Data Creation with 1,000,000,000 Personas

Reference 58

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source=pdf_text observed=2026-08-07T04:55:39.303687Z digest=sha256:018883b064d265d1b53d1b2064c243d0ce2755ac483481c854224e6ae5aa8d09

Observation 45258c39-e762-4c0e-9870-47c535fa60f4 · outbound

This paper cites Koala: A dialogue model for academic research.

Towards Efficient and Effective Alignment of Large Language Models Koala: A dialogue model for academic research

Reference 59

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source=pdf_text observed=2026-08-07T04:55:39.386153Z digest=sha256:726c8a4de5896de703e8581db01bd3a6b16c2d8ecbe7184df130858ec6e0b2e7

Observation c395c387-ca5e-4b6f-9322-8e9065136efc · outbound

This paper cites Did aristotle use a laptop? A question answering benchmark with implicit rea- soning strategies.

Towards Efficient and Effective Alignment of Large Language Models Did aristotle use a laptop? A question answering benchmark with implicit rea- soning strategies

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source=pdf_text observed=2026-08-07T04:55:39.471185Z digest=sha256:7ae9f56979d8be302abffecf06e898008914a34b44d6ad66466dc7fde20b2a8d

Observation 05ffb229-30b1-4835-97db-4bc9ddd22642 · outbound

This paper cites ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks.

Towards Efficient and Effective Alignment of Large Language Models ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks

Reference 61

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source=pdf_text observed=2026-08-07T04:55:39.571857Z digest=sha256:55cc04e7e970f023141b90fd0098e72306511a7527268a1d4e7d8f3350b3033a

Observation a597135c-ec96-4b60-8f72-8079c0c7de82 · outbound

This paper cites SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization.

Towards Efficient and Effective Alignment of Large Language Models SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization

Reference 62

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source=pdf_text observed=2026-08-07T04:55:39.649815Z digest=sha256:dcec065a51660d9ac9410e870fb91b6127867329eb4090c8dda01ac6445d8390

Observation 4b054aa4-b404-47a1-9c83-d2591fb12aba · outbound

This paper cites English gigaword.

Towards Efficient and Effective Alignment of Large Language Models English gigaword

Reference 64

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source=pdf_text observed=2026-08-07T04:55:39.744866Z digest=sha256:004cf3349c0b56677f96636768a697eb4a2bad2893b5e6bbf641e7dde9378432

Observation 5bf6af9f-735a-4f6d-bb65-7038a8c7026c · outbound

This paper cites A Survey on LLM-as-a-Judge.

Towards Efficient and Effective Alignment of Large Language Models A Survey on LLM-as-a-Judge

Reference 65

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source=pdf_text observed=2026-08-07T04:55:39.813545Z digest=sha256:04150f994b5698a34d9119f90d8f4be178fe95929fe2d8ca697dcf8282a914f5

Observation 67e14756-5e5a-4a91-b91d-d0c912ee003d · outbound

This paper cites The False Promise of Imitating Proprietary LLMs.

Towards Efficient and Effective Alignment of Large Language Models The False Promise of Imitating Proprietary LLMs

Reference 66

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source=pdf_text observed=2026-08-07T04:55:39.919359Z digest=sha256:b0d815c3b8d00ae7dbb916e5190776c958e3cd8cf6db6769859525edcbd8a7ff

Observation 17e89c8a-7a3a-467e-92e7-67759058212c · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Towards Efficient and Effective Alignment of Large Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 67

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source=pdf_text observed=2026-08-07T04:55:40.067581Z digest=sha256:b1a6ab1f47344531984e0efea4be5d4372f4c1e8d8b7410a43ae8c54eb20feda

Observation a1c3bdf8-4bdc-444b-ba66-8bc874dd732a · outbound

This paper cites Beyond imita- tion: Leveraging fine-grained quality signals for alignment.

Towards Efficient and Effective Alignment of Large Language Models Beyond imita- tion: Leveraging fine-grained quality signals for alignment

Reference 68

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source=pdf_text observed=2026-08-07T04:55:40.217742Z digest=sha256:215b9added616ae3a938e6414def7305e243cb37a4a52dd44f542b39454c745d

Observation 7c130163-e5d2-404b-b541-1bd4e4c5ed9d · outbound

This paper cites Coopera- tive inverse reinforcement learning.

Towards Efficient and Effective Alignment of Large Language Models Coopera- tive inverse reinforcement learning

Reference 69

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source=pdf_text observed=2026-08-07T04:55:40.344439Z digest=sha256:159dd3bb2ca72b08e46c5f7c4863cb15c487ee05169a0b0ab54ac23695ba7ba9

Observation 2a29c6f4-e4a9-45d9-9aaa-af8afe755c70 · outbound

This paper cites Aging with grace: Lifelong model editing with discrete key- value adaptors.

Towards Efficient and Effective Alignment of Large Language Models Aging with grace: Lifelong model editing with discrete key- value adaptors

Reference 70

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source=pdf_text observed=2026-08-07T04:55:40.428933Z digest=sha256:cd85d2fb2c5eb5c8a9796a35eeb73b4cfcc2da942dc3ab73677fb48dd7157fc1

Observation 6da72c2a-db4f-40a8-8c63-0693d739ca3b · outbound

This paper cites Does localization inform editing? surprising differences in causality-based localization vs.

Towards Efficient and Effective Alignment of Large Language Models Does localization inform editing? surprising differences in causality-based localization vs

Reference 71

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source=pdf_text observed=2026-08-07T04:55:40.544821Z digest=sha256:574b4b5cf17fef49a2b37370fbb0956c56e9dd1535eb214755c86fbea1e60bf9

Observation 26507359-384b-4a0d-96e1-0992e7bf13a5 · outbound

This paper cites Measuring massive multitask language understanding.

Towards Efficient and Effective Alignment of Large Language Models Measuring massive multitask language understanding

Reference 72

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source=pdf_text observed=2026-08-07T04:55:40.625109Z digest=sha256:d086e1a64c13830166b21bdee0a3c7acf076ff01a1bd829b788a31e56fd32b6c

Observation c7e206f8-3635-444a-b363-ff65ccab6c83 · outbound

This paper cites Measuring mathematical problem solving with the math dataset.

Towards Efficient and Effective Alignment of Large Language Models Measuring mathematical problem solving with the math dataset

Reference 73

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source=pdf_text observed=2026-08-07T04:55:40.743384Z digest=sha256:a4d219a8b3351f464a5d1019d1f762b8f0779f0a91f509c9c0bab6e2d3f02515

Observation d7a15b53-fe4c-4178-897d-94d96d60c1cc · outbound

This paper cites Knowledge distil- lation with adversarial samples supporting decision boundary.

Towards Efficient and Effective Alignment of Large Language Models Knowledge distil- lation with adversarial samples supporting decision boundary

Reference 74

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source=pdf_text observed=2026-08-07T04:55:40.833002Z digest=sha256:cc8b637aefb6b1a6bf48910dfb96c0715028e30bbc999f190583d1ad8d6044cd

Observation 7e6670d9-1673-4f20-884d-4131418befcc · outbound

This paper cites Orpo: Monolithic preference optimiza- tion without reference model.

Towards Efficient and Effective Alignment of Large Language Models Orpo: Monolithic preference optimiza- tion without reference model

Reference 75

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source=pdf_text observed=2026-08-07T04:55:40.937312Z digest=sha256:1fc2810195731b013eb45d7e037411b47754652018d615bcfe27556bc2e0c4b5

Observation 0087c7e8-a8ad-4ebe-b022-d24ee74472cd · outbound

This paper cites Parameter- efficient transfer learning for nlp.

Towards Efficient and Effective Alignment of Large Language Models Parameter- efficient transfer learning for nlp

Reference 76

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source=pdf_text observed=2026-08-07T04:55:41.059879Z digest=sha256:35eae4dd06976870703a421df9187c59f39a4c68419098850bb63fe40d778a80

Observation 402a4af6-40ad-40e3-a551-ea01e5a81b92 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

Towards Efficient and Effective Alignment of Large Language Models LoRA: Low-rank adaptation of large language models

Reference 77

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source=pdf_text observed=2026-08-07T04:55:41.200362Z digest=sha256:68bb85d0bc7e8601035e64f64d38f2072f68334325d0bf83ad84e34ccb1988bb

Observation ee0fa57b-89d2-4bf9-b945-d8b416985796 · outbound

This paper cites Is chatgpt better than human annotators? potential and limitations of chatgpt in explaining implicit hate speech.

Towards Efficient and Effective Alignment of Large Language Models Is chatgpt better than human annotators? potential and limitations of chatgpt in explaining implicit hate speech

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source=pdf_text observed=2026-08-07T04:55:41.313569Z digest=sha256:a9ebc41a5b747f67a9fb183dd0c75b68dccf358f68de6680572b94c333010500

Observation ee95b239-2885-4582-8cd6-4481d5800d58 · outbound

This paper cites Deep q-networks.

Towards Efficient and Effective Alignment of Large Language Models Deep q-networks

Reference 79

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source=pdf_text observed=2026-08-07T04:55:41.513883Z digest=sha256:09d5db49a4e1d97632be1f306e15f4c10b74e423fb0bd45524c0a1d610308658

Observation 7daef58a-0866-472a-995f-316f8004916c · outbound

This paper cites C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models.

Towards Efficient and Effective Alignment of Large Language Models C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models

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Observation 1abe27ca-14dd-4c0e-8082-b16488d5c3ce · outbound

This paper cites Smith, Iz Beltagy, and Han- naneh Hajishirzi.

Towards Efficient and Effective Alignment of Large Language Models Smith, Iz Beltagy, and Han- naneh Hajishirzi

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source=pdf_text observed=2026-08-07T04:55:41.747410Z digest=sha256:2d5c340d9d860869e10c8d5ea30703db98a5e5d4e0012b1cc8cad1b5bc67cca9

Observation 180d9a92-bf39-47c3-beb0-9ec2378a5c96 · outbound

This paper cites Aligner: Achieving efficient alignment through weak-to-strong correction.

Towards Efficient and Effective Alignment of Large Language Models Aligner: Achieving efficient alignment through weak-to-strong correction

Reference 82

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source=pdf_text observed=2026-08-07T04:55:41.824542Z digest=sha256:86065752f70904a53d2515331eeb7b5bf272907761a11b2619cc60809a9bd40e

Observation 4476b5a5-3f25-4d18-b90f-72717c8a72c8 · outbound

This paper cites Mistral 7b.

Towards Efficient and Effective Alignment of Large Language Models Mistral 7b

Reference 83

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source=pdf_text observed=2026-08-07T04:55:41.891033Z digest=sha256:468f3fb44899b79bf0027b90a18443158c7ee0c204911652cb20d44008b49e4a

Observation 1b7b75c7-ae68-4aa3-9b3a-c2a82a818f46 · outbound

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

Towards Efficient and Effective Alignment of Large Language Models What disease does this patient have? a large-scale open domain ques- tion answering dataset from medical exams

Reference 84

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source=pdf_text observed=2026-08-07T04:55:41.993198Z digest=sha256:c928d26c8accf36bb29402b1c2cbe400fd982f6cb585c15dd9319db60b1abf8b

Observation 122b1fbb-215f-463c-b615-4a683574074e · outbound

This paper cites Plus disease in retinopathy of prematurity: im- proving diagnosis by ranking disease severity and using quantitative image analy- sis.

Towards Efficient and Effective Alignment of Large Language Models Plus disease in retinopathy of prematurity: im- proving diagnosis by ranking disease severity and using quantitative image analy- sis

Reference 85

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source=pdf_text observed=2026-08-07T04:55:42.122707Z digest=sha256:fb6060414d2526a48e8b033465c5aab2ca6c940bedc9ab4d7f56438b86d8af90

Observation 20d8c7ec-1a6b-4cbc-b948-e40b86c96ebf · outbound

This paper cites Scaling Laws for Neural Language Models.

Towards Efficient and Effective Alignment of Large Language Models Scaling Laws for Neural Language Models

Reference 86

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source=pdf_text observed=2026-08-07T04:55:42.209573Z digest=sha256:4b59dcd73d936cb5ad2429ff549c3abe42240089cd16617b7aab76f040ac7c44

Observation 99a690e2-d9ff-46a8-83b2-0e764f46df61 · outbound

This paper cites A survey of reinforcement learning from human feedback.

Towards Efficient and Effective Alignment of Large Language Models A survey of reinforcement learning from human feedback

Reference 87

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source=pdf_text observed=2026-08-07T04:55:42.300537Z digest=sha256:3ae9542c7ece8846cdf43873b46b7472149176ec9c6075657b5b744b5c8d11a4

Observation 7d79b45b-d7eb-497d-961b-2158e8f2b86f · outbound

This paper cites QASC: A dataset for question answering via sentence composition.

Towards Efficient and Effective Alignment of Large Language Models QASC: A dataset for question answering via sentence composition

Reference 88

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source=pdf_text observed=2026-08-07T04:55:42.373458Z digest=sha256:d60e6d95cf14802318ca04e2c35b577dbf1fce7aa2ef1110aa48785d2906f063

Observation fccded32-3950-43d9-ae9e-e1a1d997972a · outbound

This paper cites Adam: A method for stochastic optimization.

Towards Efficient and Effective Alignment of Large Language Models Adam: A method for stochastic optimization

Reference 89

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source=pdf_text observed=2026-08-07T04:55:42.435240Z digest=sha256:dabf86034d69ae0c909383aa1df114a0ce37a97532a1e5a3cefc0627d8e7bb43

Observation 1037a898-9a40-4c2b-a389-d9ed6a1b4821 · outbound

This paper cites MAWPS: A math word problem repository.

Towards Efficient and Effective Alignment of Large Language Models MAWPS: A math word problem repository

Reference 90

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source=pdf_text observed=2026-08-07T04:55:42.491049Z digest=sha256:a52ce8feafcbe9c892b5a8a9dcf991b7600ed6b80c502a734922cab2d719d571

Observation f79c7443-f797-4bc8-9e2e-e91697403498 · outbound

This paper cites Openassistant conversations-democratizing large language model alignment.

Towards Efficient and Effective Alignment of Large Language Models Openassistant conversations-democratizing large language model alignment

Reference 91

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source=pdf_text observed=2026-08-07T04:55:42.554902Z digest=sha256:66ab38396fb85d3181d0c0d5589974d7372cb034e11a61c65770e77e9807fc98

Observation 755e83e5-26cb-425e-92d1-4a0bbda2db33 · outbound

This paper cites Spoc: Search-based pseudocode to code.

Towards Efficient and Effective Alignment of Large Language Models Spoc: Search-based pseudocode to code

Reference 92

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source=pdf_text observed=2026-08-07T04:55:42.671056Z digest=sha256:2e14b9a5ec93d03bc8fa24394f3980609bf8ab950ea2fc92271ab8742e908bcb

Observation 372e1cc8-c5a1-4a4b-b150-799272fb5cfa · outbound

This paper cites MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models.

Towards Efficient and Effective Alignment of Large Language Models MT-Eval: A Multi-Turn Capabilities Evaluation Benchmark for Large Language Models

Reference 93

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source=pdf_text observed=2026-08-07T04:55:42.704868Z digest=sha256:f425f79cfbb8ccd697e9131644465d8e032969b340e4a1bb6dc59972b57f886b

Observation dfbe63b4-05e6-4cb6-b276-20b88d796c00 · outbound

This paper cites Large language models in law: A survey.

Towards Efficient and Effective Alignment of Large Language Models Large language models in law: A survey

Reference 94

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source=pdf_text observed=2026-08-07T04:55:42.709802Z digest=sha256:ca4a6ab54e6218f431a9aecc92167a9386693e4b540b0ade88b64ce95266493d

Observation db55201a-1d75-436c-ab20-2d0a6f6d4370 · outbound

This paper cites Ds-1000: A natural and reliable benchmark for data science code generation.

Towards Efficient and Effective Alignment of Large Language Models Ds-1000: A natural and reliable benchmark for data science code generation

Reference 95

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source=pdf_text observed=2026-08-07T04:55:42.713868Z digest=sha256:e93637927253e5d75fd34c6af6ef2923d1092917d03bff8fc02d2c96d1448c2a

Observation 94a113b0-53e8-43f2-a4fd-ce0854cdf8a7 · outbound

This paper cites RLAIF vs.

Towards Efficient and Effective Alignment of Large Language Models RLAIF vs

Reference 96

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source=pdf_text observed=2026-08-07T04:55:42.718727Z digest=sha256:3ca3fb13c798aef58d97978cbfd9227643ad9b81b16393046882de449f1a8a70

Observation 4fc3d327-5031-45a9-bc38-86ab82cea280 · outbound

This paper cites Scalable agent alignment via reward modeling: a research direction.

Towards Efficient and Effective Alignment of Large Language Models Scalable agent alignment via reward modeling: a research direction

Reference 97

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source=pdf_text observed=2026-08-07T04:55:42.722920Z digest=sha256:1ac240c9e5124d01afbcd904d4ac0c26d569f8f054634d55102886865954f9b2

Observation 7d2b17f9-d8ae-44a0-89d7-61cf01e0f55f · outbound

This paper cites Zero-shot relation extraction via reading comprehension.

Towards Efficient and Effective Alignment of Large Language Models Zero-shot relation extraction via reading comprehension

Reference 98

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source=pdf_text observed=2026-08-07T04:55:42.727112Z digest=sha256:0a9b3ee8c4ea9dce6fc9399086186475eff63bf7c4c947c226505f5f99795738

Observation b1f96cdb-4c54-4951-9213-17ec4f5c7fdc · outbound

This paper cites an unresolved cited work.

Towards Efficient and Effective Alignment of Large Language Models Unresolved cited work

Reference 99

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source=pdf_text observed=2026-08-07T04:55:42.731509Z digest=sha256:d9db5e7a9de06757a729ad2bf73c830b67a3614c2133d85a54ff2cf6548c14cf

Observation 8ce389d0-653d-4a00-8892-d18212bab4e2 · outbound

This paper cites CMMLU: Measuring massive multitask language understanding in Chinese.

Towards Efficient and Effective Alignment of Large Language Models CMMLU: Measuring massive multitask language understanding in Chinese

Reference 100

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source=pdf_text observed=2026-08-07T04:55:42.735264Z digest=sha256:1acdb22adb3eb333a9a49a1bfce37348d82f9538024caf85729817ebe1ee6c85

Observation dcb59c4d-3e81-407a-8581-e3209625dafb · outbound

This paper cites Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models.

Towards Efficient and Effective Alignment of Large Language Models Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

Reference 101

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source=pdf_text observed=2026-08-07T04:55:42.739229Z digest=sha256:74454095dd5ce8a1829055e82bdf3a82e6b5d8a8c11124d10dcd6bdf101e65a9

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