activity
20142026
most citedBeyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

565 citations · 984 across the 75 of their papers we have counts for

collaborators
Showing 2023Show all

23 papers · 1 filter

cs.CV2023★ 3 cited

Holodeck: Language Guided Generation of 3D Embodied AI Environments

Yue Yang, Fan-Yun Sun, Luca Weihs +11

3D simulated environments play a critical role in Embodied AI, but their creation requires expertise and extensive manual effort, restricting their diversity and scope. To mitigate…

cs.CL2023★ 2 cited

Grounded Intuition of GPT-Vision's Abilities with Scientific Images

Alyssa Hwang, Andrew Head, Chris Callison-Burch

GPT-Vision has impressed us on a range of vision-language tasks, but it comes with the familiar new challenge: we have little idea of its capabilities and limitations. In our study…

cs.CL2023★ 3 cited

CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization

Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Peter Jansen +5

Language agents have shown some ability to interact with an external environment, e.g., a virtual world such as ScienceWorld, to perform complex tasks, e.g., growing a plant, witho…

cs.CL2023

Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneck

Josh Magnus Ludan, Qing Lyu, Yue Yang +3

Black-box deep neural networks excel in text classification, yet their application in high-stakes domains is hindered by their lack of interpretability. To address this, we propose…

cs.SE2023★ 1 cited

Kani: A Lightweight and Highly Hackable Framework for Building Language Model Applications

Andrew Zhu, Liam Dugan, Alyssa Hwang +1

Language model applications are becoming increasingly popular and complex, often including features like tool usage and retrieval augmentation. However, existing frameworks for suc…

cs.AI2023★ 1 cited

Choice-75: A Dataset on Decision Branching in Script Learning

Zhaoyi Joey Hou, Li Zhang, Chris Callison-Burch

Script learning studies how stereotypical events unfold, enabling machines to reason about narratives with implicit information. Previous works mostly consider a script as a linear…