Towards Provably Unbiased LLM Judges via Bias-Bounded Evaluation | ScienceToStartup | ScienceToStartup

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References (33)

[1]

Calibrating LLM Judges: Linear Probes for Fast and Reliable Uncertainty Estimation

2025Bhaktipriya Radharapu, Eshika Saxena et al.

[2]

Beyond Consensus: Mitigating the Agreeableness Bias in LLM Judge Evaluations

2025Suryaansh Jain, Umair Z. Ahmed et al.

[3]

When Judgment Becomes Noise: How Design Failures in LLM Judge Benchmarks Silently Undermine Validity

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"Ensure fairness in AI feedback loops with bias-bounded LLM judges, guaranteeing reduced harm from measurable biases."

LLM Evaluation•Score: 7•View PDF ↗

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Towards Provably Unbiased LLM Judges via Bias-Bounded Evaluation

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References (33)

Founder's Pitch

"Ensure fairness in AI feedback loops with bias-bounded LLM judges, guaranteeing reduced harm from measurable biases."

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Towards Provably Unbiased LLM Judges via Bias-Bounded Evaluation

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References (33)

Founder's Pitch

"Ensure fairness in AI feedback loops with bias-bounded LLM judges, guaranteeing reduced harm from measurable biases."

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