Show HN: LlmSHAP – Multi-threaded input importance for prompts and RAG context (github.com)

A new tool called llmSHAP has been announced, presenting a multi-threaded framework for explainability in large language model (LLM) outputs using Shapley values. This package enables developers to assess the importance of various inputs in prompts and retrieve augmented generation (RAG) contexts efficiently. Users can install llmSHAP with optional dependencies and leverage its capabilities to analyze model responses, generate output heatmaps, and evaluate the impact of specific input elements, all while benefiting from enhanced performance through multi-threading support. The significance of llmSHAP lies in its advanced modular architecture and the ability to perform exact Shapley value calculations, a major improvement over existing tools. Unlike other explainability frameworks, llmSHAP offers features such as generation caching and the ability to pin permanent context, which makes it particularly useful for applications requiring detailed input attribution. With this tool, AI/ML practitioners can gain deeper insights into LLM decision-making processes, paving the way for more interpretable AI systems and fostering trust in automated responses.