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cs.CL2025
Measuring what Matters: Construct Validity in Large Language Model Benchmarks
Andrew M. Bean, Ryan Othniel Kearns, Angelika Romanou +39
Evaluating large language models (LLMs) is crucial for both assessing their capabilities and identifying safety or robustness issues prior to deployment. Reliably measuring abstrac…
cs.CL2025
ATEB: Evaluating and Improving Advanced NLP Tasks for Text Embedding Models
Simeng Han, Frank Palma Gomez, Tu Vu +6
Traditional text embedding benchmarks primarily evaluate embedding models' capabilities to capture semantic similarity. However, more advanced NLP tasks require a deeper understand…