LLM Judges Exhibit Score Range Bias
xennygrimmato_ · x · 2026-07-15
This ACL paper discusses a new bias in LLM-as-a-Judge: score range bias.
Core Findings
- When applying an equivalent shift to the scoring range of the judge model (e.g., changing 1-5 to 2-6), the model's correlation with human judgments systematically changes.
- This shift is independent of the evaluated content, indicating a previously under-documented evaluation distortion in judge models.
- The phenomenon is observable across different model families, including Llama-3 and Qwen2.5, and holds true across various parameter scales.
Paper's Approach
The authors propose using contrastive decoding to mitigate this bias.
This post relays the release of a PatronusAI paper at ACL 2026, pointing to reliability issues inherent in evaluation methodologies rather than a specific model's leaderboard score.
More from Research
- Datology webinar: Curation Studio matched top data baselines with under 20% compute — josh_wills · 2026-10-27
- KANLab: an interactive browser playground for Kolmogorov-Arnold Networks — Lukas Niehaus · 2026-10-27
- CMU's LLM2Cert synthesizes stability certificates verified via SMT and Lean — CarnegieMellonU · 2026-10-27
- NVIDIA open-sources FLORA, a robotics data engine ~125x cheaper than GPT-6 Astra — nvidia · 2026-10-27
- RSNA 2026 Deep Learning Labs return: eight hands-on AI classes over four days in Chicago — PRouzrokh · 2026-10-27
- Steering two orthogonal concept neurons makes a model compose "gnome" — Sauers_ · 2026-10-27