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From the 1 of 17 linked papers with an AI index.

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20242026
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cs.CL2026

SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding

Yuqi Yang, Weiqi Wang, Baixuan Xu +13

Session history is a common way of recording user interacting behaviors throughout a browsing activity with multiple products. For example, if an user clicks a product webpage and…

cs.CL2025

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…

cs.CL2025

InteGround: On the Evaluation of Verification and Retrieval Planning in Integrative Grounding

Cheng Jiayang, Qianqian Zhuang, Haoran Li +4

Grounding large language models (LLMs) in external knowledge sources is a promising method for faithful prediction. While existing grounding approaches work well for simple queries…

cs.CL2025

Structuring the Unstructured: A Systematic Review of Text-to-Structure Generation for Agentic AI with a Universal Evaluation Framework

Zheye Deng, Chunkit Chan, Tianshi Zheng +3

The evolution of AI systems toward agentic operation and context-aware retrieval necessitates transforming unstructured text into structured formats like tables, knowledge graphs,…

cs.CL2025

Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs

Yangning Li, Weizhi Zhang, Yuyao Yang +17

Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge, yet it falls short on problems that demand multi-step inf…

cs.CL2025

XToM: Exploring the Multilingual Theory of Mind for Large Language Models

Chunkit Chan, Yauwai Yim, Hongchuan Zeng +14

Theory of Mind (ToM), the ability to infer mental states in others, is pivotal for human social cognition. Existing evaluations of ToM in LLMs are largely limited to English, negle…