575 citations
- University of WashingtonUS32 papers
- Carnegie Mellon UniversityUS21 papers
- Allen Institute for Brain ScienceUS16 papers
- Allen Institute for Artificial IntelligenceUS11 papers
- Stanford UniversityUS7 papers
- Johns Hopkins UniversityUS5 papers
- Massachusetts Institute of TechnologyUS5 papers
- California Institute of TechnologyUS4 papers
- Google (United States)US4 papers
- Harvard UniversityUS4 papers
- University of TorontoCA4 papers
- Cold Spring Harbor LaboratoryUS3 papers
6 papers · 1 filter
LinkML: An Open Data Modeling Framework
Sierra A. T. Moxon, Harold Solbrig, Nomi L. Harris +33
Scientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, non-standardized spreadsheets, or data r…
Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing
Sanchaita Hazra, Doeun Lee, Bodhisattwa Prasad Majumder +1
Large Language Models have seen expanding application across domains, yet their effectiveness as assistive tools for scientific writing - an endeavor requiring precision, multimoda…
Reasoning Up the Instruction Ladder for Controllable Language Models
Zishuo Zheng, Vidhisha Balachandran, Chan Young Park +2
As large language model (LLM) based systems take on high-stakes roles in real-world decision-making, they must reconcile competing instructions from multiple sources within a singl…
Event Causality Identification with Synthetic Control
Haoyu Wang, Fengze Liu, Jiayao Zhang +2
Event causality identification (ECI), a process that extracts causal relations between events from text, is crucial for distinguishing causation from correlation. Traditional appro…
Surfacing Semantic Orthogonality Across Model Safety Benchmarks: A Multi-Dimensional Analysis
Jonathan Bennion, Shaona Ghosh, Mantek Singh +1
Various AI safety datasets have been developed to measure LLMs against evolving interpretations of harm. Our evaluation of five recently published open-source safety benchmarks rev…
Slow Transition to Low-Dimensional Chaos in Heavy-Tailed Recurrent Neural Networks
Yi Xie, Stefan Mihalas, Łukasz Kuśmierz
Growing evidence suggests that synaptic weights in the brain follow heavy-tailed distributions, yet most theoretical analyses of recurrent neural networks (RNNs) assume Gaussian co…