elder-plinius/CL4R1T4S
CL4R1T4S is a project that provides transparency by collecting and sharing the hidden system prompts and guidelines used by major AI models and agents to promote trust and understanding of AI behavior.
Awesome AI Agents › LLM Research Repositories
xlang-ai/xlang-paper-reading
The project "XLang Paper Reading" is a curated collection of research papers focused on the development and evaluation of language model agents through executable language grounding. Executable Language Grounding (XLANG) is a concept that involves transforming natural language instructions into executable code or actions that can operate in real-world environments. These environments include databases, web applications, and physical robotic systems. The goal of XLANG is to enable language model agents or natural language interfaces to interact with and learn from these environments, facilitating human interaction with data analysis, web applications, and robotic instructions via conversational interfaces. The project highlights recent advances in XLANG, which incorporate techniques such as large language models (LLMs) combined with external tools, code generation, semantic parsing, and interactive dialog systems. These techniques empower language model agents to perform complex tasks by grounding language in executable actions. The repository organizes the research papers into thematic groups, including LLM code generation, LLM agents with tool use, LLM web grounding, and LLM robotics and embodied AI. This categorization helps researchers and practitioners keep track of the latest developments in each subfield of executable language grounding. The project also provides links to community resources such as Slack and Discord channels, encouraging collaboration and discussion among researchers interested in XLANG. The visual overview included in the repository illustrates the concept of executable language grounding and its applications. Overall, this project serves as a valuable resource for anyone interested in the intersection of natural language processing, code generation, and real-world agent interaction, providing a comprehensive and organized collection of relevant academic papers and community engagement opportunities.
https://github.com/xlang-ai/xlang-paper-reading
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