11 papers
LEKA:LLM-Enhanced Knowledge Augmentation
Xinhao Zhang, Jinghan Zhang, Fengran Mo +3
Humans excel in analogical learning and knowledge transfer and, more importantly, possess a unique understanding of identifying appropriate sources of knowledge. From a model's per…
Dynamic and Adaptive Feature Generation with LLM
Xinhao Zhang, Jinghan Zhang, Banafsheh Rekabdar +3
The representation of feature space is a crucial environment where data points get vectorized and embedded for subsequent modeling. Thus the efficacy of machine learning (ML) algor…
Retrieval-Augmented Feature Generation for Domain-Specific Classification
Xinhao Zhang, Jinghan Zhang, Fengran Mo +4
Feature generation can significantly enhance learning outcomes, particularly for tasks with limited data. An effective way to improve feature generation is to expand the current fe…
Blind Spot Navigation in Large Language Model Reasoning with Thought Space Explorer
Jinghan Zhang, Fengran Mo, Tharindu Cyril Weerasooriya +4
Large language models have shown strong reasoning capabilities through chain-structured methods such as Chain-of-Thought. Recent studies optimize thought structures by generating p…
Data-Efficient Symbolic Regression via Foundation Model Distillation
Wangyang Ying, Jinghan Zhang, Haoyue Bai +5
Discovering interpretable mathematical equations from observed data (a.k.a. equation discovery or symbolic regression) is a cornerstone of scientific discovery, enabling transparen…
Galaxy: A Cognition-Centered Framework for Proactive, Privacy-Preserving, and Self-Evolving LLM Agents
Chongyu Bao, Ruimin Dai, Yangbo Shen +4
Intelligent personal assistants (IPAs) such as Siri and Google Assistant are designed to enhance human capabilities and perform tasks on behalf of users. The emergence of LLM agent…