1 citations · 2 across the 10 of their papers we have counts for
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Scaling Domain Data Repetition in LLM Pretraining
Jingwei Li, Xinran Gu, Rui Dai +5
As large language models scale, their training-token budgets must also increase to maintain an appropriate tokens-per-parameter ratio (\(\mathrm{TPP}\)). However, high-quality doma…
Online Scheduling with a Stochastic Signal
Romain Cosson, Jingwei Li, Alexander Lindermayr +1
Nonclairvoyant scheduling is a fundamental online model in which processing times are initially unknown to the scheduler. Unfortunately, for important objectives such as total comp…
Capacity-Aware Mixture Law Enables Efficient LLM Data Optimization
Jingwei Li, Xinran Gu, Jingzhao Zhang
A data mixture refers to how different data sources are combined to train large language models, and selecting an effective mixture is crucial for optimal downstream performance. E…
Hallucination is a Consequence of Space-Optimality: A Rate-Distortion Theorem for Membership Testing
Anxin Guo, Jingwei Li
Large language models often hallucinate with high confidence on "random facts" that lack inferable patterns. We formalize the memorization of such facts as a membership testing pro…