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Found 7 papers, 1 papers with code
The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systems
Across a diverse set of LLMs, we find that while larger models obtain higher accuracy on our benchmark, they do not become more honest.
EnigmaEval: A Benchmark of Long Multimodal Reasoning Challenges
As language models master existing reasoning benchmarks, we need new challenges to evaluate their cognitive frontiers.
MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMs
We present MultiChallenge, a pioneering benchmark evaluating large language models (LLMs) on conducting multi-turn conversations with human users, a crucial yet underexamined capability for their applications.
A Careful Examination of Large Language Model Performance on Grade School Arithmetic
Large language models (LLMs) have achieved impressive success on many benchmarks for mathematical reasoning.
Parametric Matrix Models
We present a general class of machine learning algorithms called parametric matrix models.
Ranked #8 on
Image Classification
on EMNIST-Balanced
Machine Learning in Nuclear Physics
Advances in machine learning methods provide tools that have broad applicability in scientific research.
Self-learning Emulators and Eigenvector Continuation
The key ingredient is a fast estimate of the emulator error that becomes progressively more accurate as the emulator is improved, and the accuracy of the error estimate can be corrected using machine learning.
