1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.LG2026★ 1 cited
The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systems
Richard Ren, Arunim Agarwal, Mantas Mazeika +13
As large language models (LLMs) become more capable and agentic, the requirement for trust in their outputs grows significantly, yet at the same time concerns have been mounting th…
cs.SE2025
Benchmarking Failures in Tool-Augmented Language Models
Eduardo Treviño, Hugo Contant, James Ngai +2
The integration of tools has extended the capabilities of language models (LMs) beyond vanilla text generation to versatile scenarios. However, tool-augmented language models (TaLM…