7 papers
CritiCal: Can Critique Help LLM Uncertainty or Confidence Calibration?
Qing Zong, Jiayu Liu, Tianshi Zheng +7
Accurate confidence calibration in Large Language Models (LLMs) is critical for safe use in high-stakes domains, where clear verbalized confidence enhances user trust. Traditional…
INFERENCEDYNAMICS: Efficient Routing Across LLMs through Structured Capability and Knowledge Profiling
Haochen Shi, Tianshi Zheng, Weiqi Wang +6
Large Language Model (LLM) routing is a pivotal technique for navigating a diverse landscape of LLMs, aiming to select the best-performing LLMs tailored to the domains of user quer…
Towards Multi-Agent Reasoning Systems for Collaborative Expertise Delegation: An Exploratory Design Study
Baixuan Xu, Chunyang Li, Weiqi Wang +6
Designing effective collaboration structure for multi-agent LLM systems to enhance collective reasoning is crucial yet remains under-explored. In this paper, we systematically inve…
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora
Jiaxin Bai, Wei Fan, Qi Hu +17
We present AutoSchemaKG, a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. Our system leverages large language models t…
The Curse of CoT: On the Limitations of Chain-of-Thought in In-Context Learning
Tianshi Zheng, Yixiang Chen, Chengxi Li +7
Chain-of-Thought (CoT) prompting has been widely recognized for its ability to enhance reasoning capabilities in large language models (LLMs). However, our study reveals a surprisi…
Patterns Over Principles: The Fragility of Inductive Reasoning in LLMs under Noisy Observations
Chunyang Li, Weiqi Wang, Tianshi Zheng +1
Inductive reasoning, a cornerstone of human cognition, enables generalization from limited data but hasn't yet been fully achieved by large language models (LLMs). While modern LLM…