31 citations · 88 across the 22 of their papers we have counts for
3 papers · 1 filter
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…
Do as I do (Safely): Mitigating Task-Specific Fine-tuning Risks in Large Language Models
Francisco Eiras, Aleksandar Petrov, Philip H. S. Torr +2
Recent research shows that fine-tuning on benign instruction-following data can inadvertently undo the safety alignment process and increase a model's propensity to comply with har…
Language Model Tokenizers Introduce Unfairness Between Languages
Aleksandar Petrov, Emanuele La Malfa, Philip H. S. Torr +1
Recent language models have shown impressive multilingual performance, even when not explicitly trained for it. Despite this, there are concerns about the quality of their outputs…