20 citations · 27 across the 5 of their papers we have counts for
5 papers
Distinguishing Performance From Competence in Evaluations of Humanlike Abstract Reasoning
Claas Beger, Ryan Yi, Shuhao Fu +5
AI reasoning models have exceeded human performance on the ARC-AGI-1 benchmark, but does that mean state-of-the-art models have the underlying competence (humanlike abstract reason…
Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Tasks
Melanie Mitchell, Alessandro B. Palmarini, Arseny Moskvichev
We explore the abstract reasoning abilities of text-only and multimodal versions of GPT-4, using the ConceptARC benchmark [10], which is designed to evaluate robust understanding a…
NarrativeXL: A Large-scale Dataset For Long-Term Memory Models
Arseny Moskvichev, Ky-Vinh Mai
We propose a new large-scale (nearly a million questions) ultra-long-context (more than 50,000 words average document length) reading comprehension dataset. Using GPT 3.5, we summa…
Updater-Extractor Architecture for Inductive World State Representations
Arseny Moskvichev, James A. Liu
Developing NLP models traditionally involves two stages - training and application. Retention of information acquired after training (at application time) is architecturally limite…
Reinforcement Communication Learning in Different Social Network Structures
Marina Dubova, Arseny Moskvichev, Robert Goldstone
Social network structure is one of the key determinants of human language evolution. Previous work has shown that the network of social interactions shapes decentralized learning i…