AI Agent Testing Changes: New Tools Needed for Complex AI

AI agents now need special tools for testing, unlike basic AI. Learn how testing is changing for complex AI tasks.

LLM-as-a-Judge: How to Build an Automated Evaluation Pipeline You Can Trust | HackerNoon

Learn how to build an LLM-as-a-Judge pipeline with LangChain and Claude to score helpfulness and correctness at production scale.

Task-free intelligence testing of LLMs (Part 1)

Evaluating Evaluation Metrics -- The Mirage of Hallucination Detection

Hallucinations pose a significant obstacle to the reliability and widespread adoption of language models, yet their accurate measurement remains a persistent challenge. While many task- and domain-specific metrics have been proposed to assess faithfulness and factuality concerns, the robustness and generalization of these metrics are still untested. In this paper, we conduct a large-scale empirical evaluation of 6 diverse sets of hallucination detection metrics across 4 datasets, 37 language models from 5 families, and 5 decoding methods. Our extensive investigation reveals concerning gaps in current hallucination evaluation: metrics often fail to align with human judgments, take an overtly myopic view of the problem, and show inconsistent gains with parameter scaling. Encouragingly, LLM-based evaluation, particularly with GPT-4, yields the best overall results, and mode-seeking decoding methods seem to reduce hallucinations, especially in knowledge-grounded settings. These findings underscore the need for more robust metrics to understand and quantify hallucinations, and better strategies to mitigate them.

Toward Holistic Evaluation of LLMs: Integrating Human Feedback with Traditional Metrics | HackerNoon

A deep dive into why LLMs need both metrics and human feedback for real-world accuracy, ethics, and performance.