Summary of Enhancing Llm Factual Accuracy with Rag to Counter Hallucinations: a Case Study on Domain-specific Queries in Private Knowledge-bases, by Jiarui Li and Ye Yuan and Zehua Zhang
Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases
by Jiarui Li, Ye Yuan, Zehua Zhang
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an end-to-end system design to improve the factual accuracy of Large Language Models (LLMs) for domain-specific and time-sensitive queries related to private knowledge-bases using Retrieval Augmented Generation (RAG). The system integrates RAG pipeline with upstream datasets processing and downstream performance evaluation. To address LLM hallucinations, the authors finetune models with a curated dataset originating from CMU’s resources, annotated with a teacher model. Experimental results demonstrate the effectiveness of this approach in generating more accurate answers to domain-specific and time-sensitive inquiries. The findings also highlight the limitations of fine-tuning LLMs with small-scale and skewed datasets. This research showcases the potential of RAG systems in augmenting LLMs with external datasets for improved performance in knowledge-intensive tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to make Large Language Models (LLMs) more accurate when answering specific questions that require up-to-date information. The researchers created a system that combines two techniques, RAG and curated datasets, to help LLMs provide better answers. They tested their approach with a dataset from CMU and found it improved the accuracy of the LLMs’ responses. This work shows how combining different approaches can lead to better results in understanding complex information. |
Keywords
* Artificial intelligence * Fine tuning * Rag * Retrieval augmented generation * Teacher model
