Language Models are Few-Shot Butlers
By Vincent Micheli, Franccois Fleuret. 2021
44 citations recorded by the source corpus.
Pretrained language models demonstrate strong performance in most NLP tasks when fine-tuned on small task-specific datasets. Hence, these autoregressive models constitute ideal agents to operate in text-based environments where language understanding and generative capabilities are essential. Nonetheless, collecting expert demonstrations in such environments is a time-consuming endeavour. We introduce a two-stage procedure to learn from a small set of demonstrations and further improve by interacting with an environment. We show that language models fine-tuned with only 1.2% of the expert demonstrations and a simple reinforcement learning algorithm achieve a 51% absolute improvement in success rate over existing methods in the ALFWorld environment.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Large Language Models, Prompting & Reasoning
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In Artificial Intelligence
- Sergey Levine
- Chelsea Finn
- Andy Zeng
- Brian Ichter
- Karol Hausman
- Pierre Sermanet
- Pieter Abbeel
- Igor Mordatch
In Computer Vision and Pattern Recognition
- Sergey Levine
- Luc Van Gool
- Trevor Darrell
- Li Fei-Fei
- Jitendra Malik
- Abhinav Kumar Gupta
- Xiaogang Wang
- Dacheng Tao
In Large Language Models
Citations in the corpus
- Inner Monologue: Embodied Reasoning through Planning with Language Models (2022, background): “Various prior works have explored using language as a space for planning [51, 52, 53, 20, 54, 21].”
- LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models (2022, background): “Some are due to the underlying evaluation setup [13, 35, 24], while others try to train a model to predict a list of admissible actions in the environment [1].”