LLM-as-a-Judge

LLM-as-a-Judge studies how language models can assess the quality, correctness, safety, or usefulness of other models’ outputs, from chatbot responses to agent trajectories and professional work. Common approaches use explicit rubrics or reference answers, pairwise and listwise preference rankings, multi-judge panels, and trained or calibrated reward models. These methods are tested against human ratings on benchmarks for instruction following, factuality, safety, and domain-specific tasks, with research also exploring uncertainty estimates, abstention, and targeted human review. A central challenge is that consistency or agreement among judges does not guarantee alignment with people: shared biases, prompt sensitivity, weak evidence grounding, and adversarial manipulation can make scores unreliable or misleading.