most citedMINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge

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cs.CL2025

Rethinking Stateful Tool Use in Multi-Turn Dialogues: Benchmarks and Challenges

Hongru Wang, Wenyu Huang, Yufei Wang +7

Existing benchmarks that assess Language Models (LMs) as Language Agents (LAs) for tool use primarily focus on stateless, single-turn interactions or partial evaluations, such as t…

cs.CL2024★ 1 cited

MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge

Jie He, Nan Hu, Wanqiu Long +2

Large language models (LLMs) have demonstrated impressive capabilities in various reasoning tasks but face significant challenges with complex, knowledge-intensive multi-hop querie…

cs.CL2024

Question Answering Over Spatio-Temporal Knowledge Graph

Xinbang Dai, Huiying Li, Nan Hu +4

Spatio-temporal knowledge graphs (STKGs) enhance traditional KGs by integrating temporal and spatial annotations, enabling precise reasoning over questions with spatio-temporal dep…