BayesiaLab & Hellixia Innovations with GenAI

This new three-day course program is designed to help you employ Generative AI in the context of Bayesian networks using Hellixia, BayesiaLab's GenAI assistant. Hellixia offers a powerful set of capabilities to streamline the design, analysis, and documentation of knowledge models.

The course focuses on the five core functions of Hellixia, giving you practical skills to integrate them into your modeling workflows. These core functions either use internal knowledge embedded in Large Language Models (LLMs) or combine it with specific knowledge files, similar to Retrieval-Augmented Generation (RAG).

1. LLM Knowledge Mining

LLM Knowledge Mining taps into the knowledge embedded in Large Language Models (LLMs) and can automatically generate a wide variety of network types, including:

• Semantic Networks
• Semantic Flowcharts
• Causal Semantic Diagrams
• Knowledge Graphs
• (Risk) Causal Networks.

2. LLM-Augmented Machine Learning

LLM-Augmented Machine Learning can identify causal relationships between nodes and automatically add arcs to a network and/or propose Structural Priors. Hellixia also generates narratives to explain these relationships for easier interpretation and subsequent validation by domain experts.

In this context, Hellixia can also propose meaningful names for newly induced factors (latent variables) generated by BayesiaLab's Multiple-Clustering function. Similarly, Hellixia can suggest names for segments identified with BayesiaLab's Data Clustering function.

3. LLM-Powered Brainstorming Assistants

LLM-Powered Brainstorming Assistants support and accelerate brainstorming sessions by leveraging dimensions provided by subject matter experts, e.g., via BEKEE. Hellixia uses these inputs to construct a semantic network that organizes, clusters, and defines the dimensions, thereby becoming a crucial tool for deciding which dimensions to include in a model.

In the quantitative phase, Hellixia assists in the elicitation of Root Priors and ICI Local Effects, providing probability values, confidence levels, and explanatory texts.

4. LLM Text-Driven Causal Discovery

The LLM Text-Driven Causal Discovery function analyzes unstructured textual data, such as customer reviews, transcripts, survey responses, or knowledge documents, to extract structured insights from LLMs. Hellixia identifies key drivers and themes, organizes them into a semantic network, and elicits Root Priors and ICI Local Effects for each dimension, thus enabling a full driver analysis and a subsequent optimization. This mirrors the brainstorming workflow with subject matter experts (see BEKEE) but relies entirely on LLM intelligence, making it ideal when expert input is unavailable or when handling large volumes of freeform text.

5. LLM-Enhanced Network Documentation Tools

LLM-Enhanced Network Documentation Tools simplify and enrich the documentation and presentation of networks. Hellixia generates Node Comments, Long Names, and narratives about relationships. Furthermore, it supports the multilingual translation of networks and creates icons for nodes based on their semantic content.

About the Instructor

Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has worked in Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks.

After co-founding Bayesia in 2001, he and his team have been working full-time on the development of BayesiaLab, which has since emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. BayesiaLab enjoys broad acceptance in academic communities, business, and industry.