activity
20062025
most citedEmergent Tool Use From Multi-Agent Autocurricula

379 citations · 1.2k across the 11 of their papers we have counts for

collaborators

12 papers

cs.CL2025

Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems

Shang-Chi Tsai, Yun-Nung Chen

With the advancement of large language models, many dialogue systems are now capable of providing reasonable and informative responses to patients' medical conditions. However, whe…

cs.CL2024★ 164 cited

GPT-4o System Card

OpenAI, :, Aaron Hurst +416

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's…

cs.CV2024

Towards Artwork Explanation in Large-scale Vision Language Models

Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2

Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…

cs.CL2023

GPT-4 Technical Report

OpenAI, Josh Achiam, Steven Adler +278

We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-wor…

cs.LG2022

AutoDIME: Automatic Design of Interesting Multi-Agent Environments

Ingmar Kanitscheider, Harri Edwards

Designing a distribution of environments in which RL agents can learn interesting and useful skills is a challenging and poorly understood task, for multi-agent environments the di…

cs.LG2021★ 9 cited

Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft

Ingmar Kanitscheider, Joost Huizinga, David Farhi +9

An important challenge in reinforcement learning is training agents that can solve a wide variety of tasks. If tasks depend on each other (e.g. needing to learn to walk before lear…