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Paper

LLM attribution analysis across different fine-tuning strategies and model scales for automated code compliance

by Jack Wei Lun Shi arxiv/2604.15589

Free2AITools Nexus Index

36.3

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0

P: Popularity 0

R: Recency 71

Q: Quality 60

Tech Context

Vital Performance —

Existing research on large language models (LLMs) for automated code compliance has primarily focused on performance, treating the models as black boxes and overlooking how training decisions affect their interpretive behavior. This paper addresses this gap by employing a perturbation-based attribution analysis to compare the interpretive behaviors of LLMs across different fine-tuning strategies such as full fine-tuning (FFT), low-rank adaptation (LoRA) and quantized LoRA fine-tuning, as well...

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Paper Information Summary
Entity Passport
Registry ID	2604.15589
License	arXiv
Provider	arxiv

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{arxiv_2604_15589,
  author = {Jack Wei Lun Shi},
  title = {LLM attribution analysis across different fine-tuning strategies and model scales for automated code compliance Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2604.15589}},
  note = {Accessed via Free2AITools.}
}

APA Style

Jack Wei Lun Shi. (2026). LLM attribution analysis across different fine-tuning strategies and model scales for automated code compliance [Paper]. Free2AITools. https://arxiv.org/abs/2604.15589

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0

Popularity (P) 0

Recency (R) 71

Quality (Q) 60

💬 Index Insight

FNI V2.0 for LLM attribution analysis across different fine-tuning strategies and model scales for automated code compliance: Authority (A:0), Popularity (P:0), Recency (R:71), Quality (Q:60). Semantic (S) is a query-time baseline scored live at search.

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HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

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📝 Executive Summary

"Existing research on large language models (LLMs) for automated code compliance has primarily focused on performance, treating the models as black boxes and overlooking how training decisions affect their interpretive behavior. This paper addresses this gap by employing a perturbation-based attribution analysis to compare the interpretive behaviors of LLMs across different fine-tuning strategies such as full fine-tuning (FFT), low-rank adaptation (LoRA) and quantized LoRA fine-tuning, as well..."

❝ Cite Node

@article{Shi2026LLM,
  title={LLM attribution analysis across different fine-tuning strategies and model scales for automated code compliance},
  author={Jack Wei Lun Shi},
  journal={arXiv preprint arXiv:2604.15589},
  year={2026}
}

👥 Collaborating Minds

Jack Wei Lun Shi

🔗 Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

📊 Research Signals

📅1970Published

⏱️71RecencyFNI pillar

✅60QualityFNI pillar

🗂️cs.CLField

🏷️ Research Topics

lora finetuning

📄 Data Source: arXiv ↗

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Source summary: Based on arXiv metadata. Not a recommendation.

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🛡️ Paper Transparency Report

Technical metadata sourced from upstream repositories.

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🆔 Identity & Source

id: 2604.15589
slug: 2604.15589
source: arxiv
author: Jack Wei Lun Shi
license: arXiv
tags: arxiv:cs.CL, arxiv:cs.AI, arxiv:cs.LG, llm

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

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stars: null
forks: null

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