This repository curates research papers on XLang (Executable Language Grounding), a field focused on enabling language model agents to translate natural language instructions into executable code or actions for interacting with diverse environments like databases, web applications, and robotics. It serves researchers and developers aiming to build more capable and interactive AI agents.

How It Works

XLang research centers on grounding language instructions into executable formats, leveraging techniques like LLM-powered code generation, tool use, semantic parsing, and interactive dialogue systems. This approach allows AI agents to directly interact with and learn from real-world systems, bridging the gap between human intent and machine execution.

Highlighted Details

• Paper collection covers LLM code generation, LLM agents with tool use, LLM web grounding, and LLM robotics.
• Focuses on enabling agents to interact with databases, web applications, and the physical world.
• Incorporates techniques such as LLM + external tools, code generation, semantic parsing, and dialog systems.

Maintenance & Community

This is a curated list of papers, not an active software project. Updates are likely to be driven by new research publications in the field.

Licensing & Compatibility

The repository itself contains links to external research papers and does not appear to have a specific software license. Compatibility is with the research community and the underlying technologies discussed in the papers.

Limitations & Caveats

This repository is a collection of research papers and does not provide executable code, tools, or a framework for building XLang agents. It serves as a reference guide rather than a development resource.