5 citations · 6 across the 9 of their papers we have counts for
4 papers · 1 filter
ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models
Mingda Li, Xinyu Li, Weinan Zhang +1
Uncertainty Quantification (UQ) is a promising approach to improve model reliability, yet quantifying the uncertainty of Large Language Models (LLMs) is non-trivial. In this work,…
POT: Inducing Overthinking in LLMs via Black-Box Iterative Optimization
Xinyu Li, Tianjin Huang, Ronghui Mu +2
Recent advances in Chain-of-Thought (CoT) prompting have substantially enhanced the reasoning capabilities of large language models (LLMs), enabling sophisticated problem-solving t…
Breaking the Block: Preserving Data Continuity to Train Superior SAEs for Instruct Models
Jiaming Li, Haoran Ye, Yukun Chen +5
Sparse Autoencoders (SAEs) are a cornerstone of mechanistic interpretability. Existing training methods inherit the Block Training paradigm from LLM pre-training, which introduces…
TimeSeriesGym: A Scalable Benchmark for (Time Series) Machine Learning Engineering Agents
Yifu Cai, Xinyu Li, Mononito Goswami +3
We introduce TimeSeriesGym, a scalable benchmarking framework for evaluating Artificial Intelligence (AI) agents on time series machine learning engineering challenges. Existing be…