3 papers
cs.AI2026
HOLMES: Evaluating Higher-Order Logical Reasoning in LLMs
Yucheng Wu, Jundong Xu, Mingzhen Ju +4
Logical reasoning is essential for reliable AI, yet existing benchmarks are largely first-order-logic-centric, focusing on object-level deduction over fixed predicates. This misses…
cs.CL2024
TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning
Xinyuan Lu, Liangming Pan, Yubo Ma +2
Current Large Language Models (LLMs) exhibit limited ability to understand table structures and to apply precise numerical reasoning, which is crucial for tasks such as table quest…
cs.CL2023
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers
Yuxia Wang, Revanth Gangi Reddy, Zain Muhammad Mujahid +10
The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs. In this work, we…