Core Concepts
Creating a semantic search engine for mathlib4 to enhance theorem retrieval efficiency.
Abstract
The article introduces a semantic search engine for mathlib4 to improve the accessibility of theorems. It discusses the challenges in searching for theorems in mathlib4 and presents a solution through a semantic search engine. The paper outlines the methodology, related work, and benchmarking process to evaluate the performance of various retrieval methods. Experiments demonstrate the effectiveness of query augmentation and document preparation in enhancing retrieval performance.
Abstract:
Lean interactive theorem prover facilitates formal mathematical proofs.
Mathlib4 is crucial for formalizing mathematical theories.
Challenges in searching for theorems in mathlib4 led to developing a semantic search engine.
Benchmark established to assess different search engines' performance.
Introduction:
Lean community collaborates on mathlib4, eliminating repeated formalizations.
Existing search tools struggle with informal queries, leading to time wastage.
Need identified for a semantic search engine to enhance theorem retrieval efficiency.
Methodology:
Informalization of mathlib4 theorems using large language models (LLMs).
Construction of an informal-formal database for efficient theorem retrieval.
Query augmentation process enhances context understanding and improves search results.
Results:
Evaluation of different embedding models on formal, informal, and hybrid corpora.
Query augmentation significantly improves performance across all methods.
Stats
Our search engine is expected to launch within a month, available as a cloud service.
Quotes
"Our approach involves converting formal theorems from mathlib4 into their informal counterparts."
"Query augmentation enhances query clarity and accuracy in embedding models."