# Smart Audit System Empowered by LLM

arXiv: https://arxiv.org/abs/2410.07677 · HTML: https://mlsanity.com/paper/2410.07677/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 10 Oct 2024.

## Builds on (most cited first, 9 of 9)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?](https://mlsanity.com/paper/2405.05904/) (2024)
- [An LLM Compiler for Parallel Function Calling](https://mlsanity.com/paper/2312.04511/) (2023)
- [From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting](https://mlsanity.com/paper/2309.04269/) (2023)
- [Information Retrieval: Recent Advances and Beyond](https://mlsanity.com/paper/2301.08801/) (2023)
- [Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks](https://mlsanity.com/paper/2407.21059/) (2024)
- [Text Embeddings for Retrieval From a Large Knowledge Base](https://mlsanity.com/paper/1810.10176/) (2018)

## Built on by (most cited first, 4 of 4)

- [Agentic AI Sustainability Assessment for Supply Chain Document Insights](https://mlsanity.com/paper/2511.07097/) (2025)
- [Prompt Programming for Cultural Bias and Alignment of Large Language Models](https://mlsanity.com/paper/2603.16827/) (2026)
- [Occupational Prompting Reveals Cultural Bias in Large Language Models](https://mlsanity.com/paper/2606.12443/) (2026)
- [IntelliAudit: Using Large Language Models to Evaluate Audit Controls](https://mlsanity.com/paper/2608.07688/) (2026)

## Abstract (arXiv)

Manufacturing quality audits are pivotal for ensuring high product standards in mass production environments. Traditional auditing processes, however, are labor-intensive and reliant on human expertise, posing challenges in maintaining transparency, accountability, and continuous improvement across complex global supply chains. To address these challenges, we propose a smart audit system empowered by large language models (LLMs). Our approach introduces three innovations: a dynamic risk assessment model that streamlines audit procedures and optimizes resource allocation; a manufacturing compliance copilot that enhances data processing, retrieval, and evaluation for a self-evolving manufacturing knowledge base; and a Re-act framework commonality analysis agent that provides real-time, customized analysis to empower engineers with insights for supplier improvement. These enhancements elevate audit efficiency and effectiveness, with testing scenarios demonstrating an improvement of over 24%.