The LLM-as-a-Judge literature · V1

Research synthesis

A literature synthesis records the judging ideas behind Sigma5C’s observation design, including bias controls and role-weighted panels. Judge reliability remains an observation-specific question.
Prior systems
8

Judging design

The recorded design combines generalist and specialist judges, with category weights and an additional judge for close consensus scores.

The synthesis describes randomized presentation order, response-length normalization, thinking-tag removal and family self-judging exclusions as bias controls.

Literature contributions

SystemRecorded contribution
MT-BenchMulti-turn judging
G-EvalStructured scoring criteria
Length-Controlled AlpacaEvalLength-bias controls
Arena-HardDifficult prompt selection
LLMBarAdversarial judge tests
JudgeLMFine-tuned judges
Prometheus 2Open-weight judges
SageLong-form judging

Current judging method

V2: AI-as-judge and scoring

Design scope

These are recorded design choices, rather than measured guarantees of judge agreement or correctness. Observation-specific protocols and judge disagreement remain necessary context.

Recorded thresholds

Recorded judging thresholds · score points
Median divergence
3.0
Close consensus delta
1.5

The design excludes a judge vote above the median-divergence threshold and invokes an additional judge within the close-consensus threshold. Bar lengths are relative to the larger threshold; these are design settings, rather than measured model scores.

Related observations

AI-generated & AI-scored. Every observation on this site is produced and judged by AI, and AI makes mistakes. Treat each result as a recorded observation under a declared protocol, as of its date. Method and marker legend