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Articles by Jiaming Qu
Computer Science > Computation and Language
Large language models (LLMs) are increasingly used to detect unsafe content. A common approach is to combine judgments from a panel of models to correct individual mistakes, but this benefit may disappear when every model sees the same misleading context before voting. We study this risk in a controlled two-round experiment.
Computer Science > Computation and Language
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From frontier to frugal: Evaluating self-evolution frameworks with small language models
Optimizing Large Language Models (LLMs) for production AI agent deployment demands substantial computational resources and specialized human expertise (e.g., prompt engineering). Self-evolution offers a promising solution by enabling agents to autonomously enhance capabilities through structured feedback, improving performance without expensive manual optimization. However, most existing self-evolving agents rely on costly frontier LLMs, limiting scalability.
Computer Science > Computation and Language
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Interactive taxonomy development with hybrid methods
Taxonomies organize knowledge into hierarchical structures that support effective information seeking behaviors. However, developing taxonomies in fast-evolving domains like e-commerce remains a labor-intensive process. In this paper, we present an interactive system that assists users in expanding taxonomies through automated knowledge discovery from large text corpora.
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