The First Token Is Not the Verdict: Hidden Costs of Reading LLM Judges Without Generating
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Abstract (arXiv)
Reading an LLM judge's verdict from the logits of its first generated token is cheap, requires no generation, and is exactly what constrained decoding and likelihood-scoring evaluation harnesses produce. We show that this readout distorts position bias in one direction: it overstates it in every condition we test, so figures obtained this way behave as upper bounds. The mechanism is that judges do not always lead with a verdict token, on 12% to 49% of pairs for three Qwen3 judges and under 3% for Llama-3.1-8B and Phi-3.5-mini, and forcing a read on those pairs returns whichever response was shown first rather than a judgment. Pooled over the 924 pairs where a judge did not commit, the forced read flips on 89.7% of them when the responses are swapped, against 47.5% read after generation (paired difference +0.422, 95% CI [+0.365, +0.467]). The distortion is specific to what is measured: it moves position bias by 42 points while moving judge accuracy by under one point in seven of ten conditions, so it misleads whoever audits a judge rather than whoever uses one. A second, smaller failure occurs even when the judge does lead with a verdict token, since it sometimes opens with one letter and reasons its way to the other, on 0 to 5.5% of pairs at a rate uncorrelated with compliance. We recommend reporting the rate at which a judge leads with a verdict token, which costs one forward pass and no labels, alongside any position-bias figure.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 13 references that are in the map. First posted on arXiv 4 Sep 2026.
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References · 11
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
- 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
- 2024JudgeBench: A Benchmark for Evaluating LLM-based Judgescited by 318
- 2023Evaluating Large Language Models at Evaluating Instruction Followingcited by 302
- 2023Efficient Guided Generation for Large Language Modelscited by 200
- 2024Language Model Cascades: Token-level uncertainty and beyondcited by 121
- 2024Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreementcited by 105
- 2026Reliability without Validity: A Systematic, Large-Scale Evaluation of LLM-as-a-Judge Models Across Agreement, Consistency, and Biascited by 28
- 2025Improving LLM First-Token Predictions in Multiple-Choice Question Answering via Output Prefillingcited by 6
- 2026Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Whycited by 6
- 2026LLM Judges Have Dark Current: Stratified Null and Utility Tests for LLM Judge Reliabilitycited by 6
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