A Deterministic Replacement for LLM-as-Judge in Stateful Agent Evaluation
The paper proposes a deterministic method to replace LLM-as-Judge for evaluating stateful agents, aiming to improve reliability and reproducibility in agent assessment by removing the stochastic nature of using large language models as evaluators.
Background
- LLM-as-Judge is a common evaluation method where one large language model (e.g., GPT-4) scores the outputs of another LLM system. This is useful for complex tasks but is slow, expensive, and can be unreliable (the judge may make mistakes or show bias).
- "Stateful agents" are AI systems that maintain memory across interactions (e.g., a chatbot that remembers your name or previous questions). Evaluating them is harder because you must check the whole conversation history, not just one answer.
- This paper proposes a deterministic (rule-based, non-ML) replacement for the LLM judge in evaluating stateful agents. It aims to be faster, cheaper, and more consistent while still catching key errors.
- The approach is relevant as LLM-based agents become more common in production (customer support, coding assistants, etc.), where reliable automated evaluation is needed but current methods are too costly or flaky.