# Knowledge Retrieval Based on Generative AI

arXiv: https://arxiv.org/abs/2501.04635 · HTML: https://mlsanity.com/paper/2501.04635/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 9 of its 11 references that are in the map.
- First posted on arXiv 8 Jan 2025.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model](https://mlsanity.com/paper/2311.17487/) (2023)
- [Advancing the Evaluation of Traditional Chinese Language Models: Towards a Comprehensive Benchmark Suite](https://mlsanity.com/paper/2309.08448/) (2023)
- [Breeze-7B Technical Report](https://mlsanity.com/paper/2403.02712/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Preliminary Study of RAG for Taiwanese Historical Archives](https://mlsanity.com/paper/2511.07445/) (2025)
- [CCSK:Cognitive Convection of Self-Knowledge Based Retrieval Augmentation for Large Language Models](https://mlsanity.com/paper/2504.10498/) (2025)

## Abstract (arXiv)

This study develops a question-answering system based on Retrieval-Augmented Generation (RAG) using Chinese Wikipedia and Lawbank as retrieval sources. Using TTQA and TMMLU+ as evaluation datasets, the system employs BGE-M3 for dense vector retrieval to obtain highly relevant search results and BGE-reranker to reorder these results based on query relevance. The most pertinent retrieval outcomes serve as reference knowledge for a Large Language Model (LLM), enhancing its ability to answer questions and establishing a knowledge retrieval system grounded in generative AI. The system's effectiveness is assessed through a two-stage evaluation: automatic and assisted performance evaluations. The automatic evaluation calculates accuracy by comparing the model's auto-generated labels with ground truth answers, measuring performance under standardized conditions without human intervention. The assisted performance evaluation involves 20 finance-related multiple-choice questions answered by 20 participants without financial backgrounds. Initially, participants answer independently. Later, they receive system-generated reference information to assist in answering, examining whether the system improves accuracy when assistance is provided. The main contributions of this research are: (1) Enhanced LLM Capability: By integrating BGE-M3 and BGE-reranker, the system retrieves and reorders highly relevant results, reduces hallucinations, and dynamically accesses authorized or public knowledge sources. (2) Improved Data Privacy: A customized RAG architecture enables local operation of the LLM, eliminating the need to send private data to external servers. This approach enhances data security, reduces reliance on commercial services, lowers operational costs, and mitigates privacy risks.