Larql Query Language
Larql is a query language designed to interface with the internal database structures of large language models (LLMs). Rather than treating LLMs as opaque text-generation systems, Larql enables users to query and modify the underlying knowledge representations and computational structures that these models use to process and generate information. This approach conceptualizes LLMs as queryable databases, allowing direct access to their internal organization and learned patterns.
Purpose and Application
Larql addresses a significant limitation in how LLMs are typically accessed—as black-box systems that accept input and produce output without exposing intermediate representations. By providing a structured query interface, Larql allows researchers and developers to inspect what knowledge an LLM has learned, how information is organized within the model, and how specific queries traverse internal structures. This capability supports tasks such as knowledge extraction, model debugging, verification of learned representations, and targeted modification of model behavior.
Technical Approach
The language operates by translating user queries into operations that can access and manipulate the weights, embeddings, and activation patterns within an LLM’s architecture. Rather than requiring users to understand the low-level mechanics of neural network operations, Larql provides a higher-level abstraction that treats the model’s internal state as a structured knowledge system that can be queried using familiar database-like syntax.