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cs.LG2025
ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining
Melis Ilayda Bal, Volkan Cevher, Michael Muehlebach
Large language model pretraining is compute-intensive, yet many tokens contribute marginally to learning, resulting in inefficiency. We introduce Efficient Selective Language Model…
cs.LG2025
Adversarial Training for Defense Against Label Poisoning Attacks
Melis Ilayda Bal, Volkan Cevher, Michael Muehlebach
As machine learning models grow in complexity and increasingly rely on publicly sourced data, such as the human-annotated labels used in training large language models, they become…