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The Budget AI Researcher and the Power of RAG Chains

2025-06-14 02:40:35
Franklin Lee, Tengfei Ma

Abstract

Navigating the vast and rapidly growing body of scientific literature is a formidable challenge for aspiring researchers. Current approaches to supporting research idea generation often rely on generic large language models (LLMs). While LLMs are effective at aiding comprehension and summarization, they often fall short in guiding users toward practical research ideas due to their limitations. In this study, we present a novel structural framework for research ideation. Our framework, The Budget AI Researcher, uses retrieval-augmented generation (RAG) chains, vector databases, and topic-guided pairing to recombine concepts from hundreds of machine learning papers. The system ingests papers from nine major AI conferences, which collectively span the vast subfields of machine learning, and organizes them into a hierarchical topic tree. It uses the tree to identify distant topic pairs, generate novel research abstracts, and refine them through iterative self-evaluation against relevant literature and peer reviews, generating and refining abstracts that are both grounded in real-world research and demonstrably interesting. Experiments using LLM-based metrics indicate that our method significantly improves the concreteness of generated research ideas relative to standard prompting approaches. Human evaluations further demonstrate a substantial enhancement in the perceived interestingness of the outputs. By bridging the gap between academic data and creative generation, the Budget AI Researcher offers a practical, free tool for accelerating scientific discovery and lowering the barrier for aspiring researchers. Beyond research ideation, this approach inspires solutions to the broader challenge of generating personalized, context-aware outputs grounded in evolving real-world knowledge.

Abstract (translated)

在浩瀚且迅速增长的科学文献海洋中,对于初出茅庐的研究者来说,导航是一项艰巨的任务。目前支持研究想法生成的方法往往依赖于通用的大规模语言模型(LLMs)。虽然这些模型在帮助理解与总结方面表现优异,但在指导用户产生实用的研究想法上却常常受限。为此,在这项研究中,我们提出了一种新颖的结构化框架来促进研究构思——“预算AI研究员”(The Budget AI Researcher)。 该框架采用检索增强生成(RAG)链、向量数据库以及主题引导配对技术,从数百篇机器学习论文的概念中重新组合产生新的想法。系统会摄入来自九个主要人工智能会议的论文,并将这些涵盖机器学习各个子领域的文献组织成一个层次化的主题树。借助这棵树,“预算AI研究员”能够识别远端的主题搭配,生成新颖的研究摘要并反复自我评估以确保与相关文献和同行评审保持一致。这一过程使得产生的研究摘要既能基于现实世界中的研究成果,又具有明确的创新性。 实验结果显示,相对于标准提示方法,我们的方法在提高生成研究想法的具体性和实用性方面取得了显著进步。人类评价进一步证明了输出的有趣程度有了实质性的提升。“预算AI研究员”通过弥合学术数据与创意生成之间的鸿沟,为加速科学发现提供了一种实用且免费的工具,并降低了有志于科研的年轻人进入门槛。 除了促进研究构思外,该方法还激发了解决更广泛的挑战——即如何生成个性化的、情境相关的输出并基于不断变化的实际知识进行构建。

URL

https://arxiv.org/abs/2506.12317

PDF

https://arxiv.org/pdf/2506.12317.pdf


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