5 citations · 6 across the 9 of their papers we have counts for
9 papers
Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting
Xinyu Li, Yuanyuan Wang, Haoxuan Li +7
Causal discovery aims to uncover causal structures from observational data, which is crucial for real-world decision-making. However, different causal discovery algorithms can prod…
SRA: Semantic Relation-Aware Flowchart Question Answering
Xinyu Li, Bowei Zou, Yuchong Chen +2
Flowchart Question Answering (FlowchartQA) is a multi-modal task that automatically answers questions conditioned on graphic flowcharts. Current studies convert flowcharts into int…
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…
Large Language Models for Automatic Detection of Sensitive Topics
Ruoyu Wen, Stephanie Elena Crowe, Kunal Gupta +6
Sensitive information detection is crucial in content moderation to maintain safe online communities. Assisting in this traditionally manual process could relieve human moderators…
Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models
Mingda Li, Xinyu Li, Yifan Chen +2
Although Retrieval-Augmented Large Language Models (RALMs) demonstrate their superiority in terms of factuality, they do not consistently outperform the original retrieval-free Lan…
Personalized Language Modeling from Personalized Human Feedback
Xinyu Li, Ruiyang Zhou, Zachary C. Lipton +1
Personalized large language models (LLMs) are designed to tailor responses to individual user preferences. While Reinforcement Learning from Human Feedback (RLHF) is a commonly use…