LLM Evaluation
LLM Evaluation is the process of assessing language models on their capability, safety, and factuality using benchmarks, human judgment, and automated metrics.
Definition
Evaluation includes automated benchmarks that test specific capabilities, human review of output quality, and metrics like perplexity that measure how well the model predicts text. Evaluation reveals whether the model has learned desired behaviors, where it fails, and how it compares to other models.
Continuous evaluation during development guides improvements: if a model fails on reasoning tasks, developers optimize for reasoning. If a model hallucinates, developers apply hallucination mitigation. The evaluation process directly shapes which capabilities are enhanced and what trade-offs are accepted.
Why it matters for AI visibility
LLM evaluation practices determine what qualities answer engines optimize for. If evaluation emphasizes source citation accuracy, models will be trained to cite carefully. If evaluation overlooks outdated information, models won't prioritize freshness. Understanding evaluation metrics helps predict which content characteristics matter.
Related terms
AI Benchmarks
AI Benchmarks are standardized evaluation tasks and datasets used to measure and compare LLM capabilities across reasoning, knowledge, language understanding, and specific domain tasks.
AILarge Language Model (LLM)
Large Language Models are neural networks trained on massive text datasets to predict and generate human language. They form the foundation of modern AI search and answer engines.
AIRLHF
RLHF (Reinforcement Learning from Human Feedback) is a training technique that uses human feedback to improve LLM outputs. Humans rank model responses, guiding the model toward preferred behaviors.
AIHallucination Mitigation
Hallucination Mitigation refers to techniques that reduce false outputs in LLM responses, including grounding in retrieved sources, fact-checking, and training adjustments. No method fully eliminates hallucination.