Paper Citation Record · LEDGER

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation

As of 8 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2506.15702.

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

pith.paper-citation-record.v1
2506.15702 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:40:25.825552Z

measured 37 of 37 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-16T11:55:50.897500Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T11:55:51.079688Z

Reference resolution

36 of 36 outbound references displayed

verified exact5
verified fuzzy8
unresolved22
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b9924e1f-e1e8-4d68-ae66-05b90dd5e44a · outbound

This paper cites Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:40:28.392773Z

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.

Observation 2166bd8d-13a3-4ede-b97d-d8758d1b375e · outbound

This paper cites Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:21.984686Z

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Observation 36d6d8aa-9349-4e21-82e5-716189de53a6 · outbound

This paper cites Distill and replay for continual language learning.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Distill and replay for continual language learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:40:28.237234Z

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.

Observation 0136af4c-2c5a-43b4-bca9-15aa968675cf · outbound

This paper cites Scalable Language Model with Generalized Continual Learning.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Scalable Language Model with Generalized Continual Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:22.174315Z

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Observation d27f66ef-674d-47ad-9826-b7b39c8ef32e · outbound

This paper cites Continual Learning of Large Language Models: A Comprehensive Survey.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Continual Learning of Large Language Models: A Comprehensive Survey

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:22.228275Z

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Observation ada8e617-2b84-4a42-81c6-6ece30a7ae44 · outbound

This paper cites The Llama 3 Herd of Models.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation The Llama 3 Herd of Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:22.273316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 532801a0-376d-414e-9637-7faeb31f872b · outbound

This paper cites Parameter-efficient transfer learning for nlp.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Parameter-efficient transfer learning for nlp

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:22.325270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03c095da-ffbb-4ca9-9bf5-dac60207c9b0 · outbound

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

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation LoRA: Low-Rank Adaptation of Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:22.403153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e690718-46dd-42f6-b1a3-406a06b9cc7a · outbound

This paper cites DoRA: Weight-Decomposed Low-Rank Adaptation.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation DoRA: Weight-Decomposed Low-Rank Adaptation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:22.478505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4dce038a-5dd7-40d0-a31c-5a324fb5d4c7 · outbound

This paper cites Mitigating the alignment tax of rlhf, 2024.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Mitigating the alignment tax of rlhf, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:40:28.001975Z

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.

Observation 131f8fd6-9ce0-4c47-9148-a405da58db28 · outbound

This paper cites Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:22.685924Z

Source-reported events for the cited work

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Observation e32bdeb5-a6b0-4a28-9b14-b1b1c7ddcb8a · outbound

This paper cites Evaluating Language Model Finetuning Techniques for Low-resource Languages.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Evaluating Language Model Finetuning Techniques for Low-resource Languages

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:40:27.015634Z

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.

Observation 56542d17-5577-4ebc-bd31-b1ba87260097 · outbound

This paper cites Fine-tuning and Utilization Methods of Domain-specific LLMs.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Fine-tuning and Utilization Methods of Domain-specific LLMs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:22.930321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 534cfb67-86f9-4389-8742-ee595b0b2694 · outbound

This paper cites Harnessing pre-trained neural networks with rules for formality style transfer.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Harnessing pre-trained neural networks with rules for formality style transfer

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:40:27.784303Z

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.

Observation b23a30d4-143b-4992-bb28-64b1b23c7ccf · outbound

This paper cites Zero: Memory optimizations toward training trillion parameter models.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Zero: Memory optimizations toward training trillion parameter models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:23.184609Z

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Observation 8d64e5a8-a154-4bab-af04-d1c6479caf9d · outbound

This paper cites OpenELM: An Efficient Language Model Family with Open Training and Inference Framework.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation OpenELM: An Efficient Language Model Family with Open Training and Inference Framework

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:23.283185Z

Source-reported events for the cited work

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Observation 9bb6b675-a755-4d53-8141-790b5eed44ef · outbound

This paper cites GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, March 2021.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, March 2021

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:40:27.545350Z

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.

Observation 11a82b97-dbbc-4169-b6ec-fca206eccbb0 · outbound

This paper cites Textbooks Are All You Need.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Textbooks Are All You Need

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:23.446594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21135899-76bd-4f7a-8647-ee4c8f05deb1 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:23.571712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe04fba2-d9cc-488d-b5e8-d4df9cfc286d · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Gemma: Open Models Based on Gemini Research and Technology

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:23.706659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e664326-fcfb-4341-93bd-a253106a72d5 · outbound

This paper cites Compact Language Models via Pruning and Knowledge Distillation.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Compact Language Models via Pruning and Knowledge Distillation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:23.840008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cdce6dd-8b70-4fad-a826-1650997c087c · outbound

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

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:23.979348Z

Source-reported events for the cited work

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Observation cc2d33b2-30b3-4a3d-9087-042ebf2fee62 · outbound

This paper cites Pmc open access subset, 2024.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Pmc open access subset, 2024

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:40:27.471214Z

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.

Observation 524998d2-3a98-4b65-99c1-2dd8583bbe0a · outbound

This paper cites Pile of law: Learning responsible data filtering from the law and a 256gb open-source legal dataset.Advances in Neural Information Processing Systems , 35:29217–29234, 2022.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Pile of law: Learning responsible data filtering from the law and a 256gb open-source legal dataset.Advances in Neural Information Processing Systems , 35:29217–29234, 2022

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:40:27.459928Z

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.

Observation d09cf018-66b7-4013-89e9-aaaf55cc60bd · outbound

This paper cites OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:24.492611Z

Source-reported events for the cited work

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Observation a18c343b-6f9f-479d-bd70-10bd5032dfee · outbound

This paper cites Openwebtext corpus.http://Skylion007.github.io/OpenWebTextCorpus, 2019.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Openwebtext corpus.http://Skylion007.github.io/OpenWebTextCorpus, 2019

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:24.639960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4eede3ac-a845-4b0c-9f0b-a20b36f93a73 · outbound

This paper cites Efficient Hierarchical Domain Adaptation for Pretrained Language Models.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Efficient Hierarchical Domain Adaptation for Pretrained Language Models

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:40:26.744067Z

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.

Observation f1304c0c-a8c7-4e9b-84bc-03d556617291 · outbound

This paper cites Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:40:26.595401Z

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.

Observation 377d1690-53c8-4f96-bc5a-5f4d6375b1cd · outbound

This paper cites Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:40:26.349803Z

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.

Observation f77d5e94-3af4-4021-ab8b-ad69c2b76e30 · outbound

This paper cites Taming pre-trained language models with n-gram representations for low-resource domain adaptation.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Taming pre-trained language models with n-gram representations for low-resource domain adaptation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:40:27.380758Z

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.

Observation c681321b-25d0-4ae3-aa35-cdf10623bde8 · outbound

This paper cites $k$NN-Adapter: Efficient Domain Adaptation for Black-Box Language Models.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation $k$NN-Adapter: Efficient Domain Adaptation for Black-Box Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:25.154848Z

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Observation a36c054a-8148-4258-b397-d8431c44bc66 · outbound

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

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:25.256358Z

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Observation 85ad0bf9-ec45-4b43-851f-dc2ee4ff90f2 · outbound

This paper cites Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:40:26.067733Z

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

Observation 0caa0043-5f84-482e-b6ae-cc4d4164c229 · outbound

This paper cites When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:25.565467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b8f02e8-aba9-43d0-b476-ed70cce6d159 · outbound

This paper cites Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T12:40:25.692005Z

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Observation 2280a8ac-60dc-470b-82a0-579ba4d0cba4 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.

Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation Overcoming catastrophic forgetting in neural networks

Reference 36

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T12:40:27.239667Z

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.

Pith citing papers

Observation 7bfa0aa3-81b3-4c95-96b7-4eddc78029c9 · inbound

Small Language Models are the Future of Agentic AI cites this paper.

Small Language Models are the Future of Agentic AI Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-16T11:55:51.081789Z

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.