83.4k citations · 96.3k across the 51 of their papers we have counts for
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Identifying the Risks of LM Agents with an LM-Emulated Sandbox
Yangjun Ruan, Honghua Dong, Andrew Wang +6
Recent advances in Language Model (LM) agents and tool use, exemplified by applications like ChatGPT Plugins, enable a rich set of capabilities but also amplify potential risks - s…
STEVE-1: A Generative Model for Text-to-Behavior in Minecraft
Shalev Lifshitz, Keiran Paster, Harris Chan +2
Constructing AI models that respond to text instructions is challenging, especially for sequential decision-making tasks. This work introduces a methodology, inspired by unCLIP, fo…
Training on Thin Air: Improve Image Classification with Generated Data
Yongchao Zhou, Hshmat Sahak, Jimmy Ba
Acquiring high-quality data for training discriminative models is a crucial yet challenging aspect of building effective predictive systems. In this paper, we present Diffusion Inv…
Residual Prompt Tuning: Improving Prompt Tuning with Residual Reparameterization
Anastasia Razdaibiedina, Yuning Mao, Rui Hou +4
Prompt tuning is one of the successful approaches for parameter-efficient tuning of pre-trained language models. Despite being arguably the most parameter-efficient (tuned soft pro…
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
Yann Dubois, Xuechen Li, Rohan Taori +6
Large language models (LLMs) such as ChatGPT have seen widespread adoption due to their strong instruction-following abilities. Developing these LLMs involves a complex yet poorly…
TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation
Zhaoyan Liu, Noel Vouitsis, Satya Krishna Gorti +2
We propose TR0N, a highly general framework to turn pre-trained unconditional generative models, such as GANs and VAEs, into conditional models. The conditioning can be highly arbi…
Boosted Prompt Ensembles for Large Language Models
Silviu Pitis, Michael R. Zhang, Andrew Wang +1
Methods such as chain-of-thought prompting and self-consistency have pushed the frontier of language model reasoning performance with no additional training. To further improve per…
Mastering Diverse Domains through World Models
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba +1
Developing a general algorithm that learns to solve tasks across a wide range of applications has been a fundamental challenge in artificial intelligence. Although current reinforc…