Large language models as judges: recent advances in LLM-based evaluation, critique, preference modeling, and feedback for text and code

Abstract Large Language Models (LLMs) are increasingly used as judges to evaluate, rank, and critique AI-generated text and code. This survey provides a comprehensive overview of recent advances (2020–early 2026) in LLM-based evaluation, covering techniques, applications, and challenges across domains. We make three main contributions: (1) a unified taxonomy of LLM judging tasks spanning text (summarization, dialogue, factuality, safety) and code (correctness checking, code review, security analysis); (2) a systematic review of prompting strategies (zero/few-shot, rubric-based, pairwise comparison, chain-of-thought) and advanced pipelines (ensemble judges, multi-agent debate, tool-augmented verification); and (3) an analysis of LLM judge quality, documenting systematic biases (length, position, self-preference) and their mitigations. We review practical applications including benchmark evaluation (MT-Bench, Chatbot Arena), data filtering, and reward modeling for RLHF/RLAIF. Key challenges discussed include calibration, fairness, reproducibility, and adversarial robustness. We conclude with future directions emphasizing standardized protocols, uncertainty estimation, and human–AI collaboration. LLM-based judging shows promise for scalable evaluation, but careful design and rigorous validation are essential to ensure these AI judges meet human standards of accuracy and fairness.