most citedA Careful Examination of Large Language Model Performance on Grade School Arithmetic

12 citations · 20 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025★ 3 cited

Remote Labor Index: Measuring AI Automation of Remote Work

Mantas Mazeika, Alice Gatti, Cristina Menghini +44

AIs have made rapid progress on research-oriented benchmarks of knowledge and reasoning, but it remains unclear how these gains translate into economic value and automation. To mea…

cs.LG2025★ 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.AI2025★ 1 cited

EnigmaEval: A Benchmark of Long Multimodal Reasoning Challenges

Clinton J. Wang, Dean Lee, Cristina Menghini +7

As language models master existing reasoning benchmarks, we need new challenges to evaluate their cognitive frontiers. Puzzle-solving events are rich repositories of challenging mu…

cs.CL2025★ 3 cited

MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMs

Ved Sirdeshmukh, Kaustubh Deshpande, Johannes Mols +7

We present MultiChallenge, a pioneering benchmark evaluating large language models (LLMs) on conducting multi-turn conversations with human users, a crucial yet underexamined capab…

cs.CL2024★ 12 cited

A Careful Examination of Large Language Model Performance on Grade School Arithmetic

Hugh Zhang, Jeff Da, Dean Lee +12

Large language models (LLMs) have achieved impressive success on many benchmarks for mathematical reasoning. However, there is growing concern that some of this performance actuall…