← Search

Lennart Luettgau

2 accepted papers

2026

Quantifying Biases in LLM-as-a-Judge Evaluations

ICML 2026poster

The evaluation of large language models (LLMs) is increasingly performed by other LLMs, a setup commonly known as "LLM-as-a-judge", or autograders. While autograders offer a scalable alternative to human evaluation, they are not free from biases (e.g., favouring longer outputs or generations from th…

Cited by 0SourceScholar
2025

Measuring what Matters: Construct Validity in Large Language Model Benchmarks

NeurIPS 2025poster

Evaluating large language models (LLMs) is crucial for both assessing their capabilities and identifying safety or robustness issues prior to deployment. Reliably measuring abstract and complex phenomena such as `safety' and `robustness' requires strong construct validity, that is, having measures t…

Cited by 0SourceScholar