Paper Citation Record · LEDGER

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs

As of 13 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 9 inbound Pith citation observations for arXiv:2412.13337.

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

pith.paper-citation-record.v1
2412.13337 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:19:19.123213Z

measured 67 of 67 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:04:55.940338Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

58 of 58 outbound references displayed

verified exact3
verified fuzzy4
unresolved51
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation ae43ba94-cbcf-4f5a-b99d-35343de8bff3 · outbound

This paper cites Physics of Language Models: Part 3.1, Knowledge Storage and Extraction.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Physics of Language Models: Part 3.1, Knowledge Storage and Extraction

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.628389Z

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Observation 05f5428e-e25f-478b-8d07-f77c5fb1e409 · outbound

This paper cites Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.800381Z

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Observation 8ef2ab61-229e-4258-8c46-7d31cd2f9faa · outbound

This paper cites Instruction Pre-Training: Language Models are Supervised Multitask Learners.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Instruction Pre-Training: Language Models are Supervised Multitask Learners

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.655811Z

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Observation 6b8bf7df-a9ac-494b-9f91-b8b818631cda · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.678386Z

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Observation 1d4f4b93-6746-4243-8c36-1e14399b683e · outbound

This paper cites Both performed similarly, with stacked training slightly outperforming phased training across all bench- marks.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Both performed similarly, with stacked training slightly outperforming phased training across all bench- marks

Reference 6

Resolution: verified exact; raw_fallback, observed 2026-08-11T13:19:19.399533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8dbc7d0a-7f59-49b9-90cd-fe8cb6c59e3c · outbound

This paper cites Phase Description # Samples Phase 00 Instruction following warmup: simple, template-based instruction-response pairs to transition the base models to instruction-following behavior.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Phase Description # Samples Phase 00 Instruction following warmup: simple, template-based instruction-response pairs to transition the base models to instruction-following behavior

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:20.897460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c781a5c7-c335-4b2d-9047-273f30a2422c · outbound

This paper cites Apple intelligence foundation language models.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Apple intelligence foundation language models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.722362Z

Source-reported events for the cited work

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Observation 06527b53-fad6-4c37-9a85-92dbe8aa7470 · outbound

This paper cites Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.736521Z

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Observation 63779e69-b0c9-47c8-b600-d2f5807b51f4 · outbound

This paper cites ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.755939Z

Source-reported events for the cited work

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Observation 2d0ed83c-217d-4157-9df7-b015adf27b00 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Measuring Massive Multitask Language Understanding

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.766781Z

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Observation 99f2e7e2-39f0-4541-a76b-8fcffdc9ccaa · outbound

This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.778809Z

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Observation 8fcd72c7-5c97-4060-ab9b-9ccbb8a3c978 · outbound

This paper cites Simple and Scalable Strategies to Continually Pre-train Large Language Models.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.786182Z

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Observation 67a753df-c9fb-4dd0-ba04-67d9a6d956cf · outbound

This paper cites This diversity reduces gradient variance, promoting stable updates and helping the model retain pre-trained knowledge without significant forgetting.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs This diversity reduces gradient variance, promoting stable updates and helping the model retain pre-trained knowledge without significant forgetting

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:20.838699Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 317e3c17-0957-46ff-8d70-7939479d683b · outbound

This paper cites Mistral 7B.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Mistral 7B

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.812023Z

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Observation 3722976b-acc4-4abd-9f67-baaaa2202ef9 · outbound

This paper cites Mixtral of Experts.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Mixtral of Experts

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.818157Z

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Observation 038ac8a1-9955-4e14-8302-7d1c7185c3ca · outbound

This paper cites Fantastic Generalization Measures and Where to Find Them.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Fantastic Generalization Measures and Where to Find Them

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.827430Z

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Observation cfa9c493-f10c-406e-9d17-60bc22225a4d · outbound

This paper cites Understanding Continual Learning Settings with Data Distribution Drift Analysis.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Understanding Continual Learning Settings with Data Distribution Drift Analysis

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.849324Z

Source-reported events for the cited work

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Observation a4b61e5c-e1fd-4943-b860-3eee95e3bf3a · outbound

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

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.870117Z

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Observation b40ea2f7-6988-4959-a58b-74721d0550b7 · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.876927Z

Source-reported events for the cited work

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Observation dbbfff60-5d2d-4a92-8c2a-9a207ab12da4 · outbound

This paper cites What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.883703Z

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Observation b44295e4-e54d-44c9-9db7-9f4020f34b5a · outbound

This paper cites The Flan Collection: Designing Data and Methods for Effective Instruction Tuning.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs The Flan Collection: Designing Data and Methods for Effective Instruction Tuning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.891682Z

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Observation c6efb0fa-c7be-4a3a-badb-16a9bf8251d9 · outbound

This paper cites Granite Code Models: A Family of Open Foundation Models for Code Intelligence.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Granite Code Models: A Family of Open Foundation Models for Code Intelligence

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.898978Z

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Observation daa68dfe-25d1-44c7-9646-12e318b4f662 · outbound

This paper cites Orca 2: Teaching Small Language Models How to Reason.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Orca 2: Teaching Small Language Models How to Reason

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.918005Z

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Observation 27027e1d-b450-4e33-96f9-165096c0ff5c · outbound

This paper cites Orca: Progressive Learning from Complex Explanation Traces of GPT-4.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Orca: Progressive Learning from Complex Explanation Traces of GPT-4

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.927083Z

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Observation 80d76ed2-a615-4e87-90d1-d2f5d47abfda · outbound

This paper cites ISBN 9781450384421.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs ISBN 9781450384421

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.933396Z

Source-reported events for the cited work

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Observation 201fb40f-38f7-4224-a236-ca10c4388bbf · outbound

This paper cites GPT-4 Technical Report.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs GPT-4 Technical Report

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.938992Z

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Observation afb8f0e5-4615-49b3-a511-c7b0d5ed7b48 · outbound

This paper cites Training language models to follow instructions with human feedback.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Training language models to follow instructions with human feedback

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.944956Z

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Observation 03e83027-c86e-425b-b72f-9d79232e2c1a · outbound

This paper cites Wei Pang, Chuan Zhou, Xiao-Hua Zhou, and Xiaojie Wang.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Wei Pang, Chuan Zhou, Xiao-Hua Zhou, and Xiaojie Wang

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:20.936609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 19d08b4e-a8f1-43af-85a8-5f19d58653fc · outbound

This paper cites Instruction Tuning with GPT-4.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Instruction Tuning with GPT-4

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.958987Z

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Observation cfa491ba-3343-4c2a-bc7c-91df6afcd161 · outbound

This paper cites A Constructive Prediction of the Generalization Error Across Scales.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs A Constructive Prediction of the Generalization Error Across Scales

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.972099Z

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Observation a87de158-bc32-4130-b990-04fe78c7f5ec · outbound

This paper cites Multitask Prompted Training Enables Zero-Shot Task Generalization.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Multitask Prompted Training Enables Zero-Shot Task Generalization

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.979564Z

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Observation 75126dd1-0758-4751-8059-1470925cacf0 · outbound

This paper cites Teven Le Scao et al.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Teven Le Scao et al

Reference 39

Resolution: verified exact; raw_fallback, observed 2026-08-11T13:19:19.808324Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0b818c71-9ec1-48f7-8fac-1af8eed94182 · outbound

This paper cites Don't Decay the Learning Rate, Increase the Batch Size.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Don't Decay the Learning Rate, Increase the Batch Size

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.995228Z

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Observation d04ef4d7-7c82-4939-a46a-3e5571cc9b40 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:19.021871Z

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Observation 9350659d-d123-43c4-bdac-3334c86ab06d · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:19.028038Z

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Observation 01bd5928-7c03-45c4-81a0-0f4805a68a06 · outbound

This paper cites A Chandra X-ray Survey of Optically Selected Close Galaxy Pairs: Unexpectedly Low Occupation of Active Galactic Nuclei.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs A Chandra X-ray Survey of Optically Selected Close Galaxy Pairs: Unexpectedly Low Occupation of Active Galactic Nuclei

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:19.034377Z

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Observation aef8f542-85f1-4ec3-9a91-d3605d1e261f · outbound

This paper cites MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:19.040669Z

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Observation b93ee87d-df18-4135-aef4-030e7673ab26 · outbound

This paper cites Finetuned Language Models Are Zero-Shot Learners.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Finetuned Language Models Are Zero-Shot Learners

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:19.050734Z

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Observation 5e27c432-41c6-4dfd-94b8-e96f3b295f4b · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 48

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Observation 2ba1ff1e-d3da-4c8b-8b89-d1765f3876a3 · outbound

This paper cites WizardLM: Empowering large pre-trained language models to follow complex instructions.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs WizardLM: Empowering large pre-trained language models to follow complex instructions

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:19.061453Z

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Observation bd19a69d-dd16-4171-abab-f17d37adbbe4 · outbound

This paper cites Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

Reference 50

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Observation e30bbe58-080c-455b-a6c6-dfd4f0833a79 · outbound

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Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Synthetic continued pretraining

Reference 51

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Observation 07bb832d-8f96-4de3-ae93-151a5137b85e · outbound

This paper cites LIMA: Less Is More for Alignment.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs LIMA: Less Is More for Alignment

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:19.078971Z

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Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-11T13:19:20.878471Z

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Observation 999315a6-9b8c-48f5-9839-63c9ecb2ea6d · outbound

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Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-11T13:19:20.859315Z

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Observation f590b67d-127f-4ecc-b04e-309d43828e11 · outbound

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Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Unresolved cited work

Reference 58

Resolution: verified exact; raw_fallback, observed 2026-08-11T13:19:19.299965Z

Source-reported events for the cited work

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Observation 4bb46fd3-315b-4f1f-9fc4-16d1649059bd · outbound

This paper cites Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio C´esar Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, et al.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio C´esar Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, et al

Reference 1966

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Observation 27934eb6-1015-4910-926d-af31b7cb0035 · outbound

This paper cites LAB: Large-Scale Alignment for ChatBots.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs LAB: Large-Scale Alignment for ChatBots

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:19.014062Z

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Observation 1fcd025d-8d6a-4785-805a-ade83a0ad5f0 · outbound

This paper cites Scaling laws for downstream task performance of large language models.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Scaling laws for downstream task performance of large language models

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:19:20.961905Z

Source-reported events for the cited work

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Observation fe0afd7b-b973-4067-9a08-721d6af60270 · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 2016

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Observation f3053140-27e1-40e4-ac3f-c12eef180cbe · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Dropout: a simple way to prevent neural networks from overfitting

Reference 2017

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Observation ec2043be-48d8-4b9b-93d6-a84968c80c65 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Training Verifiers to Solve Math Word Problems

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.688751Z

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Observation 8dea4143-f835-4c16-bb9b-163d09b842f2 · outbound

This paper cites Scaling Laws for Neural Language Models.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Scaling Laws for Neural Language Models

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.835652Z

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Observation 1173cb8a-6bf0-4339-9cb8-eec92d58a186 · outbound

This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.842637Z

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Observation 97e7064a-9418-43f8-a26a-526b3c3c1011 · outbound

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

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.856098Z

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Observation 5b6f82bb-a009-4e8f-8d8a-a8403644cf80 · outbound

This paper cites OpenAssistant Conversations -- Democratizing Large Language Model Alignment.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs OpenAssistant Conversations -- Democratizing Large Language Model Alignment

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.697794Z

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Observation 7cdea624-089f-4643-a396-32502d825ab4 · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.639273Z

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Observation 205c0304-1d3a-4eb4-9580-7c5725d57e0a · outbound

This paper cites Scaling Instruction-Finetuned Language Models.

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs Scaling Instruction-Finetuned Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T13:19:18.668913Z

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Pith citing papers

Observation e040c504-c48e-47f9-8502-6c11da144c51 · inbound

Too Big to Think: Capacity, Memorization, and Generalization in Pre-Trained Transformers cites this paper.

Too Big to Think: Capacity, Memorization, and Generalization in Pre-Trained Transformers Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:55.940338Z

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Observation afba8959-19d8-42b6-9574-00a5845b1a6e · inbound

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Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T14:58:43.493356Z

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Observation 9a21d251-f405-4f7e-8753-18363244b8e2 · inbound

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Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T16:30:44.745578Z

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Observation 54fdf153-b9f3-44e4-87d4-e4de3aec2fc7 · inbound

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Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-18T17:42:47.526644Z

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Observation 018f5302-5807-4622-8b25-cc6325cd7ad8 · inbound

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Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:51:34.943976Z

Source-reported events for the cited work

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Observation 070dbe26-0e03-41ef-bf26-b1e3584d6609 · inbound

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Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-17T05:29:05.118255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 47f62b80-dc44-4e9e-8269-1f95681662b5 · inbound

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Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:17:39.904727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 46622c25-0f5f-47b0-ae66-5f10df2355c9 · inbound

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Reference 71

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:05:57.600419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation afe23145-10fd-4949-ad1e-43ae8781db53 · inbound

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Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-02T13:56:49.004087Z

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