snap-stanford/stark
(NeurIPS D&B 2024) STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases
Combines textual search with structured relational reasoning across three large-scale knowledge bases (Amazon products, academic papers, biomedical data) using hybrid retrieval approaches. Provides pre-computed embeddings from multiple models (text-embedding-ada-002, GritLM, Colbert) and supports custom embedding generation, enabling direct evaluation of LLM retrieval performance on semi-structured data. Available as a pip package with automatic HuggingFace dataset downloads and includes an interactive knowledge base explorer for query analysis.
330 stars.
Stars
330
Forks
51
Language
Python
License
MIT
Category
Last pushed
Feb 06, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/snap-stanford/stark"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
cefriel/competence-kg
A tutorial on Knowledge Graphs discussing how to model the employee competences within a company
TIGER-AI-Lab/KB-BINDER
"Few-shot In-context Learning for Knowledge Base Question Answering" [ACL2023]
HKUST-KnowComp/FolkScope
Codes and Datasets for the ACL2023 Findings Paper: FolkScope: Intention Knowledge Graph...
pat-jj/KARE
[ICLR'25] Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval
rsinghlab/K-Paths
Official Implementation of K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug...