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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.
| Date | Stars |
|---|---|
| 2026-07-24 | 30301 |
| 2026-07-25 | 30309 |
| 2026-07-28 | 30372 |
| 2026-07-30 | 30372 |
| 2026-07-31 | 30407 |
| 2026-08-06 | 30407 |
Today
— stars today
This week
+35 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.11%/day
<p align="center"> <img src="assets/logo.svg" style="width: 25%; height: auto;"> </p> # STORM: Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking <p align="center"> | <a href="http://storm.genie.stanford.edu"><b>Research preview</b></a> | <a href="https://arxiv.org/abs/2402.14207"><b>STORM Paper</b></a>| <a href="https://www.arxiv.org/abs/2408.15232"><b>Co-STORM Paper</b></a> | <a href="https://storm-project.stanford.edu/"><b>Website</b></a> | </p> **Latest News** 🔥 - [2025/01] We add [litellm](https://github.com/BerriAI/litellm) integration for language models and embedding models in `knowledge-storm` v1.1.0. - [2024/09] Co-STORM codebase is now released and integrated into `knowledge-storm` python package v1.0.0. Run `pip install knowledge-storm --upgrade` to check it out. - [2024/09] We introduce collaborative STORM (Co-STORM) to support human-AI collaborative knowledge curation! [Co-STORM Paper](https://www.arxiv.org/abs/2408.15232) has been accepted to EMNLP 2024 main conference. - [2024/07] You can now install our package with `pip install knowledge-storm`! - [2024/07] We add `VectorRM` to support grounding on user-provided documents, complementing existing support of search engines (`YouRM`, `BingSearch`). (check out [#58](https://github.com/stanford-oval/storm/pull/58)) - [2024/07] We release demo light for developers a minimal user interface built with streamlit framework in Python, handy for local development and demo hosting (checkout [#54](https://github.com/stanford-oval/storm/pull/54)) - [2024/06] We will present STORM at NAACL 2024! Find us at Poster Session 2 on June 17 or check our [presentation material](assets/storm_naacl2024_slides.pdf). - [2024/05] We add Bing Search support in [rm.py](knowledge_storm/rm.py). Test STORM with `GPT-4o` - we now configure the article generation part in our demo using `GPT-4o` model. - [2024/04] We release refactored version of STORM codebase! We define [interface](knowledge_storm/interface.py) for STORM pipeline and reimplement STORM-wiki (check out [`src/storm_wiki`](knowledge_storm/storm_wiki)) to demonstrate how to instantiate the pipeline. We provide API to support customization of different language models and retrieval/search integration. [](https://github.com/psf/black) ## Overview [(Try STORM now!)](https://storm.genie.stanford.edu/) <p align="center"> <img src="assets/overview.svg" style="width: 90%; height: auto;"> </p> STORM is a LLM system that writes Wikipedia-like articles from scratch based on Internet search. Co-STORM further enhanced its feature by enabling human to collaborative LLM system to support more aligned and preferred information seeking and knowledge curation. While the system cannot produce publication-ready articles that often require a significant number of edits, experienced Wikipedia editors have found it helpful in their pre-writing stage. **More than 70,000 people have tried our [live research preview](https://storm.genie.stanford.edu/). Try it out to see how STORM can help your knowledge exploration journey and please provide feedback to help us improve the system 🙏!** ## How STORM & Co-STORM works ### STORM STORM breaks down generating long articles with citations into two steps: 1. **Pre-writing stage**: The system conducts Internet-based research to collect references and generates an outline. 2. **Writing stage**: The system uses the outline and references to generate the full-length article with citations. <p align="center"> <img src="assets/two_stages.jpg" style="width: 60%; height: auto;"> </p> STORM identifies the core of automating the research process as automatically coming up with good questions to ask. Directly prompting the language model to ask questions does not work well. To improve the depth and breadth of the questions, STORM adopts two strategies: 1. **Perspective-Guided Question Aski
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d7aaa3166525d82c, topic:large-language-models
matched fp:d7aaa3166525d82c, topic:nlp
matched fp:d7aaa3166525d82c, topic:retrieval-augmented-generation