PaperHelper: Knowledge-Based LLM QA Paper Reading Assistant
On the map: Retrieval-augmented generation → RAG pipeline design and evaluation
Abstract (arXiv)
In the paper, we introduce a paper reading assistant, PaperHelper, a potent tool designed to enhance the capabilities of researchers in efficiently browsing and understanding scientific literature. Utilizing the Retrieval-Augmented Generation (RAG) framework, PaperHelper effectively minimizes hallucinations commonly encountered in large language models (LLMs), optimizing the extraction of accurate, high-quality knowledge. The implementation of advanced technologies such as RAFT and RAG Fusion significantly boosts the performance, accuracy, and reliability of the LLMs-based literature review process. Additionally, PaperHelper features a user-friendly interface that facilitates the batch downloading of documents and uses the Mermaid format to illustrate structural relationships between documents. Experimental results demonstrate that PaperHelper, based on a fine-tuned GPT-4 API, achieves an F1 Score of 60.04, with a latency of only 5.8 seconds, outperforming the basic RAG model by 7% in F1 Score.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. First posted on arXiv 20 Feb 2025.
Builds on
0 of 0 references.
No references here yet.
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026Combining Retrieval-Augmented Text Generation with LLMs for Reading Content Recommendations
- 2025A RAG-Based Institutional Assistant
- 2025Navigating Through Paper Flood: Advancing LLM-based Paper Evaluation through Domain-Aware Retrieval and Latent Reasoning
- 2024Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report
- 2024LLM-Ref: Enhancing Reference Handling in Technical Writing with Large Language Models
- 2023PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
- 2024Revolutionizing Retrieval-Augmented Generation with Enhanced PDF Structure Recognition
- 2025Can Large Language Models Be Trusted Paper Reviewers? A Feasibility Study