Paper Citation Record · LEDGER

The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 28 inbound Pith citation observations for arXiv:2408.13296.

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

pith.paper-citation-record.v1
2408.13296 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 28 of 28 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T05:42:43.485261Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:38:44.330075Z

Reference resolution

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External citation measurements

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Outbound references

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Pith citing papers

Observation 9c6f4f80-49de-4fc4-8b45-3bce4c4b466d · inbound

From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap cites this paper.

From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 80

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:08:20.720723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 053c8c81-a026-4aef-8d3c-b9ee54e03466 · inbound

Large Language Models for Market Research: A Data-augmentation Approach cites this paper.

Large Language Models for Market Research: A Data-augmentation Approach The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-23T07:12:41.436915Z

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Observation 040b0cef-6164-43aa-bc92-5ac51ea9a613 · inbound

Compromising Honesty and Harmlessness in Language Models via Deception Attacks cites this paper.

Compromising Honesty and Harmlessness in Language Models via Deception Attacks The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 27

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Observation 9862e8ad-de5e-4de1-a60f-572ecbcd0629 · inbound

When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning cites this paper.

When Can Large Reasoning Models Save Thinking? Mechanistic Analysis of Behavioral Divergence in Reasoning The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 22

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Observation d34ece1a-8a34-4a56-9baa-0b71ec5b3013 · inbound

Small Language Models in the Real World: Insights from Industrial Text Classification cites this paper.

Small Language Models in the Real World: Insights from Industrial Text Classification The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 25

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Observation ff3899ac-635a-4ea3-bb34-96360efe4ed9 · inbound

GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation cites this paper.

GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 27

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Observation 710e4de9-f6e5-43fa-bdab-043426d54c44 · inbound

Localizing Persona Representations in LLMs cites this paper.

Localizing Persona Representations in LLMs The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 21

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Observation 244660e8-c889-40d0-b79e-f02cbc813d6a · inbound

Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning cites this paper.

Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 45

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Observation f8f8e842-5b86-4bf6-afa9-970827080394 · inbound

Using Diffusion Models to do Data Assimilation cites this paper.

Using Diffusion Models to do Data Assimilation The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T11:35:47.633535Z

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Observation f58b1a13-e084-4efb-a8eb-4c3c11eb7e6b · inbound

Evaluating the Effectiveness of Direct Preference Optimization for Personalizing German Automatic Text Simplifications for Persons with Intellectual Disabilities cites this paper.

Evaluating the Effectiveness of Direct Preference Optimization for Personalizing German Automatic Text Simplifications for Persons with Intellectual Disabilities The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T20:55:26.539651Z

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Observation 3f583cbe-c9ba-4f4e-ae45-27326b4c5955 · inbound

SCoRE: Streamlined Corpus-based Relation Extraction using Multi-Label Contrastive Learning and Bayesian kNN cites this paper.

SCoRE: Streamlined Corpus-based Relation Extraction using Multi-Label Contrastive Learning and Bayesian kNN The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 26

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Observation 6a9bf15d-ea06-481d-948b-419ba7b6b314 · inbound

VerilogDB: The Largest, Highest-Quality Dataset with a Preprocessing Framework for LLM-based RTL Generation cites this paper.

VerilogDB: The Largest, Highest-Quality Dataset with a Preprocessing Framework for LLM-based RTL Generation The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T18:54:05.022427Z

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AI-Assisted Fixes to Code Review Comments at Scale cites this paper.

AI-Assisted Fixes to Code Review Comments at Scale The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T16:29:47.792002Z

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Observation 0455a953-8555-4895-97f2-d3ad1b29e7f2 · inbound

GREAT: Guiding Query Generation with a Trie for Recommending Related Search about Video at Kuaishou cites this paper.

GREAT: Guiding Query Generation with a Trie for Recommending Related Search about Video at Kuaishou The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T15:39:59.071515Z

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Observation dd912f32-fbe0-41be-9ea5-176e20207c49 · inbound

LobRA: Multi-tenant Fine-tuning over Heterogeneous Data cites this paper.

LobRA: Multi-tenant Fine-tuning over Heterogeneous Data The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-05T12:56:42.976452Z

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Observation 226205e6-2371-48fa-a356-34e04c005ae9 · inbound

ReclAIm: A Multi-Agent Framework for Monitoring and Correcting Performance Decline in Medical Imaging AI cites this paper.

ReclAIm: A Multi-Agent Framework for Monitoring and Correcting Performance Decline in Medical Imaging AI The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 15

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Observation 357c4d5f-376d-4c68-bc59-72a4ade7bee4 · inbound

Efficiency vs. Alignment: Investigating Safety and Fairness Risks in Parameter-Efficient Fine-Tuning of LLMs cites this paper.

Efficiency vs. Alignment: Investigating Safety and Fairness Risks in Parameter-Efficient Fine-Tuning of LLMs The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T06:54:07.562899Z

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Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation cites this paper.

Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 22

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Observation ee33fc35-ee06-4de2-acee-8d21c0b66222 · inbound

SecureGate: Learning When to Reveal PII Safely via Token-Gated Dual-Adapters for Federated LLMs cites this paper.

SecureGate: Learning When to Reveal PII Safely via Token-Gated Dual-Adapters for Federated LLMs The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-15T22:00:21.013110Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f21c03c0-66a1-4bba-9af9-ef00efa212d4 · inbound

A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning cites this paper.

A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 39

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Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction cites this paper.

Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 39

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Observation 68cee377-3674-460d-a876-c754faef3888 · inbound

Enhancing Large Language Models with Retrieval Augmented Generation for Software Testing and Inspection Automation cites this paper.

Enhancing Large Language Models with Retrieval Augmented Generation for Software Testing and Inspection Automation The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T10:44:37.992467Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation befede9c-461f-4c1d-9e88-d2301e154369 · inbound

DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs cites this paper.

DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 60

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T02:53:29.817876Z

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Observation c65c4cba-02f6-4972-ad42-f7a73164c69e · inbound

Development and Preliminary Evaluation of a Domain-Specific Large Language Model for Tuberculosis Care in South Africa cites this paper.

Development and Preliminary Evaluation of a Domain-Specific Large Language Model for Tuberculosis Care in South Africa The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T22:49:33.905197Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9e1b11a8-e860-4f27-8504-d547b48a7520 · inbound

PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs cites this paper.

PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:51:42.728245Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c57d14c5-6024-46a3-a954-b673f0d2bb4b · inbound

Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications cites this paper.

Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T05:41:23.721116Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a81c334b-ba03-4238-ac08-11ae35949018 · inbound

Landsat-Sentinel-2 Algal Bloom Mapping Using Vision Transformers: Model Description, Implementation, and Examples cites this paper.

Landsat-Sentinel-2 Algal Bloom Mapping Using Vision Transformers: Model Description, Implementation, and Examples The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T17:38:44.331339Z

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Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization cites this paper.

Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 263

Resolution: unresolved; no resolver link, observed 2026-07-12T01:14:24.457057Z

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