563 citations
- FACE FoundationUS8 papers
- Stanford UniversityUS5 papers
- Cornell UniversityUS4 papers
- Massachusetts Institute of TechnologyUS4 papers
- Allen Institute for Artificial IntelligenceUS3 papers
- Carnegie Mellon UniversityUS3 papers
- Google (United States)US3 papers
- King Fahd University of Petroleum and MineralsSA3 papers
- Sorbonne UniversitéFR3 papers
- Allen InstituteUS2 papers
- Booz Allen Hamilton (United States)US2 papers
- École PolytechniqueFR2 papers
22 papers
What if AI systems weren't chatbots?
Sourojit Ghosh, Pranav Narayanan Venkit, Sanjana Gautam +1
The rapid convergence of artificial intelligence (AI) toward conversational chatbot interfaces marks a critical moment for the industry. This paper argues that the chatbot paradigm…
AI for Scientific Discovery is a Social Problem
Georgia Channing, Avijit Ghosh
Artificial intelligence (AI) is being increasingly applied to scientific research, but its benefits remain unevenly distributed across different communities and disciplines. While…
WorldLLM: Improving LLMs' world modeling using curiosity-driven theory-making
Guillaume Levy, Cedric Colas, Pierre-Yves Oudeyer +2
Large Language Models (LLMs) possess general world knowledge but often struggle to generate precise predictions in structured, domain-specific contexts such as simulations. These l…
GemMaroc: Unlocking Darija Proficiency in LLMs with Minimal Data
Abderrahman Skiredj, Ferdaous Azhari, Houdaifa Atou +2
Open-source large language models (LLMs) still marginalise Moroccan Arabic (Darija), forcing practitioners either to bolt on heavyweight Arabic adapters or to sacrifice the very re…
Proximal Point Nash Learning from Human Feedback
Daniil Tiapkin, Daniele Calandriello, Denis Belomestny +5
Traditional Reinforcement Learning from Human Feedback (RLHF) often relies on reward models, frequently assuming preference structures like the Bradley--Terry model, which may not…
MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces
Loris Gaven, Thomas Carta, Clément Romac +4
Open-ended learning agents must efficiently prioritize goals in vast possibility spaces, focusing on those that maximize learning progress (LP). When such autotelic exploration is…