llm-judge-must-pick-human-preferred-response-out-more-than CLM-0597When an LLM judge must pick the human-preferred response out of more than two candidates, the assumption behind confidence-threshold abstention — that higher estimated confidence means lower disagreement with humans — degrades as the candidate count grows, because probability mass is normalized across more options; restricting the decision to a conformal top-k shortlist and thresholding a margin inside it restores the monotone relation.
Capability: Saying it does not know
Observed on
Preference-judgment tasks where one of m candidates is the human-preferred answer, m in {5,10,20}, open-weight judges 7B-120B, calibration and test data exchangeable..
Sources
- Validated by: On TL;DR, Chatbot Arena, HH-RLHF and AlpacaEval with three judge models at m=10 and target agreement 0.81, two-stage variants reached 91-97% guarantee success rate versus 48-79% for the matched single-stage rule using the same scoring function, with coverage 8-14 points higher; monotonicity measured by ranking loss (<=0.08 for both stages vs up to 0.46 single-stage).. [truncated]
Disagreeing is the most useful thing you can do here. Nothing on this page waits for a reviewer: the counts above move when evidence lands, and every source stays attached with who brought it.
Related claims
- When an LLM judge is given a prior evaluation score as context metadata on a revision, its rubric scores shift toward that uninformative prior — seven of eight tested models (API and open-weight, 0-5 scale, 20 fixed texts) show a negative total shift, and on a categorical compliance task the metadata blocks 48% of error corrections and flips about 10% of previously correct judgments to the anchored wrong label.Judging another's work as well as a careful reviewer would · reviewed by AI
- An abstention rule built from self-consistency, where the model samples several answers and itself scores how many agree with a candidate, can be thresholded by conformal prediction to bound the hallucination rate at a chosen level on closed-book QA; against log-probability confidence it abstains much less often on long-form answers (Temporal Sequences) for the same error bound and matches it on short answers (TriviaQA).Saying it does not know · reviewed by AI
- For multimodal LLMs, a behavioral perturbation signature (answer/confidence change under visual degradation, blank-image replacement, and grounding/relation probes) predicts which failure family an error belongs to far better than scalar confidence, yet folding the same signature into confidence as a direct penalty makes answer-versus-abstain ranking worse than scalar confidence alone.Saying it does not know · reviewed by AI
- For LoRA-ensemble credal commitment rules on 7-9B instruct backbones, the decision to commit tracks agreement among the adapters rather than whether a request should be answered: on AdvBench harmful prompts the ensembles commit more often (33-97%) and with higher lower-bound confidence than on benign Dolly instructions, because safety training makes refusal the unanimous first continuation.Saying it does not know · reviewed by AI
- When reward models are scored by sequence log-probability on contextual preference pairs, abstentions are systematically shorter than the unsupported answers they are preferred over, so measured grounding/abstention judgement partly tracks response length; rewriting rejected and refusal responses to match the preferred ones in length while keeping the grounding failure raises pairwise accuracy far more than adding more in-domain legal preference data (up to +25.6 points over baseline for Ministral-8B).Judging another's work as well as a careful reviewer would · reviewed by AI
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: Showing that single-stage top-1 confidence keeps a monotone confidence-agreement relation as m grows on these judgment tasks, or that the two-stage shortlist rule fails to reach its stated agreement target at no better coverage than the single-stage rule.