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SHAP stands for "SHapley Additive exPlanations", and is a unified approach that explains the output of any machine learning model; by delivering cohesive explanations it provides invaluable insight into how predictions are being made and opens up immense possibilities in terms of practical applications. In this tutorial we'll explore how to use SHAP values to explain and improve ML models, delving deeper into specific use cases as we go along.
Data poisoning is a real threat to enterprise AI systems like Large Language Models (LLMs), where malicious data tampering can skew outputs and decision-making processes unnoticed. This article explores the mechanics of data poisoning attacks, real-world examples across industries, and best practices to mitigate risks through red teaming, and automated evaluation tools.
Releasing an upgraded version of Giskard's LLM scan for comprehensive vulnerability assessments of LLM applications. New features include more accurate detectors through optimized prompts and expanded multi-model compatibility supporting OpenAI, Mistral, Ollama, and custom local LLMs. This article also covers an initial setup guide for evaluating LLM apps.
As businesses increasingly integrate LLMs into several applications, ensuring the reliability of AI systems is key. LLMs can generate biased, inaccurate, or even harmful outputs if not properly evaluated. This article explains the importance of LLM evaluation, and how to do it (methods and tools). It also present Giskard's comprehensive solutions for evaluating LLMs, combining automated testing, customizable test cases, and human-in-the-loop.
Articles, tutorials and latest news on AI Quality, Security & Compliance
Our new course in collaboration with DeepLearningAI team provides training on red teaming techniques for Large Language Model (LLM) and chatbot applications. Through hands-on attacks using prompt injections, you'll learn how to identify vulnerabilities and security failures in LLM systems.
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