STORM by Stanford University: The AI Model for Academic and Research Purposes
Artificial intelligence is a swiftly evolving beast. From novel chatbots that come with the dust and are gone with the wind to behemoths like ChatGPT, AI is on the march. STORM by Stanford University is an innovative AI-powered research tool currently making waves in the global academic community and beyond.
Since early 2024, this open-source research project has helped many academics, students, and content creators craft articles from scratch. “Articles from scratch?” We hear you ask. Yes, in a nutshell, it can be used to create Wikipedia-style papers, complete with citations in a matter of minutes. Whether you’re interested in AI for schoolwork, or even AI for grad school level writing, STORM can help you on your path to a PhD.
Get set, because we’re headed for the eye of the storm to discover the origins of STORM, and the humans behind it. We’ll also go on to discuss its performance and steer you in the direction of the STORM website so you can try it out for yourself.
The nature of the STORM?
Short for “Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking”, STORM Stanford AI research project is an AI tool that can create Wikipedia style entries faster than you can make a cup of coffee. Let’s be clear, STORM is not your average B- chatbot, it is an A+ gifted-class knowledge creator and research assistant that’s ready to back up its statements and provide citations galore.
While AI is often a faceless, authorless corporate beast, the team behind STORM are actually Stanford students and faculty. STORM is created by human members of Stanford’s OVAL team, namely: Yijia Shao, Yucheng Jiang, Theodore A. Kanell, Peter Xu, Omar Khattab, and Monica S. Lam.
LLMs (large language models) may be useful for a layman’s general research. But for academics and content creators, they tend to fall short in several areas. Accuracy is king in academia, and LLMs have well publicized limitations in veracity, as well as specificity, and understanding of complex academic topics. What’s more, LLMs are renowned for producing confident, yet incorrect answers that lack citations.
The final nail in the coffin for academic use of LLMs is plagiarism. Rapid generation of text comes with the risk that the LLM is simply replicating existing academic sources. While the majority of LLMs create content via retrieval-augmented generation (RAG), STORM takes content creation several steps further to craft accurate, organized answers. Now let’s find out more about the multi-agent conversations behind every STORM search.
The multi-agent STORMversation
At time of writing, STORM is powered by Bing Search and Azure OpenAI GPT-4o-mini. This recent upgrade featuring the latest technologies enables STORM to break down the barrier between the excess of accessible information out there, and what an individual is able to assimilate. The “knowledge curation agent” explored in STORM (remember, it is still a research project) aims to provide a solid foundation for knowledge discovery, making in-depth learning possible without the stress of laborious research.
Where many LLMs are a letdown, STORM is a success. This is in no small part thanks to STORM’s multi-perspective question asking. Multiple AI agents cooperate in an agentic system, where individual AI agents perform the tasks of content retrieval, multi-perspective question asking, and finally, synthesis of content. Similar in many ways to how a human team would collaborate to research and write an ambitious project, STORM approaches complex tasks from multiple angles to create comprehensive written content that can give human-created articles a run for their money.
Various processes
STORM provides users with the option of STORM AI autonomous or Co-STORM (Human-AI collaboration), as well as search engine choices. After inputting your topic to STORM, the platform generally takes a minute or two to generate your article. Once an article is completed, a “See BrainSTORMing Process” option appears above the summary of your article. This neat feature allows users to see the AI agents (editors) and the steps they have taken to contribute to the final article.
If you do try STORM, do the good folks at Stanford a favor and provide feedback using the handy feedback box on the web demo. This information, as well as your purpose for writing the article will be securely stored, and not combined with your Google account info.
Who can STORM help?
If you’re looking for an AI tool to assist your academic writing, or just AI for school in general, then STORM is certainly worth a try. Here are a few different user groups that may find STORM more useful than regular old LLMs.
- Academics and researchers can both benefit from using STORM, as it can create structured outlines on complex academic topics that can be used as educational resources. The verification and citation features of STORM are particularly attractive for this cohort.
- Students today may lack the time to conduct their own research. With STORM, students of all levels can quickly get well-organized notes and summaries in easy to understand Wikipedia style articles, likely a form that they are already familiar with.
- Content creators with deadlines to meet or day jobs to attend to can rapidly research and organize data on STORM. Verified, fact-based outlines that offer multiple perspectives can be quickly crafted, and updated by users as topics evolve.
A STORM in a teacup?
As with all AI platforms, STORM is not without its limitations. If you’ve read this far, chances are you’re not plotting to misuse STORM to graduate from school or college. But just in case you were wondering, STORM is not (yet) an AI writing tool that can knock out a 10,000 word college-level dissertation for you. Try out STORM and you will quickly discover that the “research preview” excels in generating Wikipedia-style articles.
Similar to Wikipedia, STORM is very good at providing a comprehensive outline of a topic. The Wikipedia-esque sections are useful as foundations to build out from, but may lack specific or detailed information that some users require. This is presumably an aspect of the platform that will be improved, time will tell.
Another issue that may deter or, indeed, attract some users is STORM’s limited safety measures. The potential to generate offensive content is certainly present on STORM, and on behalf of the Stanford Open Virtual Assistant Lab team, we remind you to follow STORM’s guidelines. As with other AI content generators, mistakes are still a likelihood, so double check your info before going to print!
Become a rider on the STORM?
We trust that you have enjoyed learning about STORM today. Whatever field you work or study in, we believe that STORM is definitely worth a try. If you’re keen to join the STORMversation, simply head over to the STORM homepage. Here you’ll be able to login via your Google account, and experience the research-tool that academics from Glasgow to Gaborone are talking about.
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Edmund is an English copywriter based in New Taipei City, Taiwan. He is a widely published writer and translator with two decades of experience in the field of bridging linguistic and cultural gaps between Chinese and English.
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