Benchmarking Hallucination Detection

Benchmarking Hallucination Detection in AI Systems
Alex Thomas, Principal Data Scientist at Wisecube, explores the critical challenge of benchmarking hallucination detection in large language models (LLMs). This webinar covers:
🔹The impact and risks of AI hallucinations
🔹Different types of LLM applications (zero-context Q&A, RAG Q&A, summarization)
🔹Strategies for measuring hallucinations, including the Pythia method
🔹Challenges in benchmarking hallucination detection systems
🔹Comparison of different models (GPT-4, Llama 2) and measurement approaches
🔹The importance of choosing appropriate metrics and datasets
🔹Challenges with using existing datasets and creating custom ones
🔹Future directions in hallucination detection, including model calibration and ensembling
This talk provides valuable insights for AI practitioners looking to improve the reliability and trustworthiness of their LLM applications. Alex shares practical advice on developing, benchmarking, and tuning measurement systems for various use cases and industries.
Download the webinar slides here: hubs.ly/Q02Mhqh20
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📄 Download the webinar slides here: hubs.ly/Q02Mhqh20
🌐 Pythia Website askpythia.ai/
🛠 Activate your Pythia trial now! 👉 app.askpythia.ai/