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
The primary purpose of Larql is to provide a structured method for inspecting and manipulating the knowledge encoded within language models. By exposing internal representations—such as embeddings, attention weights, and learned associations—Larql allows researchers and developers to better understand how LLMs organize information and make predictions. This capability extends beyond simple text generation to enable more fine-grained control over model behavior and the ability to verify or audit the information stored within trained models.
Technical Approach
Larql operates by abstracting the internal mechanisms of language models into a queryable schema. Users can formulate queries that target specific aspects of a model’s learned representations, similar to how SQL queries target relational databases. This abstraction layer enables both read operations for inspection and write operations for modification, though the specifics of implementation vary depending on the underlying LLM architecture and framework.