ICML: Correcting Systematic Bias in LLM-as-Judge Evaluations
Kangwook_Lee · x · 2026-07-06
Researchers presented "LLM-as-a-Judge Corrected" at ICML to correct systematic biases when using LLMs as evaluators, aiming to improve the reliability of automated evaluations for agents and models. This collaborative work by Kangwook Lee's group addresses known limitations in current LLM-as-Judge evaluation frameworks.
More from Research
- Causal-only attention for non-generative tasks is wasteful, argues HF engineer — antoine_chaffin · 2026-09-11
- Catholic University of Chile researcher: scaling AI feedback is key to sustainable medical education — julianvarascom · 2026-09-11
- Nature paper images cellular activity across all organs, revealing body-wide circuits — arjunrajlab · 2026-09-11
- SignNet 1M Dataset Released for Sign Language Research — ducha_aiki · 2026-09-11
- ECCV26 Oral: Flow Matching Enables Single-Stage Multi-View Point Cloud Registration — ducha_aiki · 2026-09-11
- InFlux++ Method Released — ducha_aiki · 2026-09-11