LLM-as-a-Judge: When to Use Reasoning, CoT, and Explanations
Author: Aparna Dhinakaran (Arize AI)
Explanations: Worth Including
Requesting an explanation alongside a label gives three things:
- Stability: reduces variance across repeated judgments; increases human alignment
- Diagnostics: reveals judge’s decision factors — exposes position bias, verbosity bias, self-preference bias
- Reusable supervision: explanation text can train better evaluators or improve generation models
Explanation Order
Explanation-first, then label is the recommended default. Score generated in context of reasoning → more grounded. No strong dataset evidence that order changes accuracy much, but explanation-first is easy to standardize.
Chain-of-Thought (CoT)
Evidence is mixed — CoT only helps when the task genuinely requires multiple complex reasoning steps (multi-hop factual checks, reasoning over linked criteria). For most NLG evaluation tasks, CoT has neutral or negative effect on human alignment, and increases cost.
Avoid generic “think step by step” — it’s unlikely to add value. Instead, invest in clear, detailed rubrics.
CoT may help for: evaluating agent tool calling, medical QA with cross-reference checks.
Reasoning Models
Models with internal chain-of-thought (OpenAI o-series, Claude extended thinking):
- Outperform base models on multi-step tasks
- Explicit CoT prompting is unnecessary with reasoning models — just wastes tokens
- Still request explanations in output for auditing/debugging
- Cost: 3-20x token usage vs. base models → use selectively
Practical Prompting Patterns
A) Explanation-first (recommended default)
System: Evaluate summary vs. article. Brief reasoning first, then JSON
{reasoning, scores:{consistency,relevance,fluency,conciseness}, overall}
B) Label only (throughput / A-B smoke tests)
System: Return only {scores:{...}, overall}
C) Structured CoT plan (sparingly, only for complex multi-step eval)
System: Draft a 3-5 step evaluation plan, execute it, return {plan, reasoning, scores, overall}
Key Principle
Explanations make LLM evaluation more transparent. Ordering explanation before label makes it easier to review. In most NLG tasks, explicit CoT adds little beyond a well-structured prompt.