Web: http://arxiv.org/abs/2209.08141

Sept. 20, 2022, 1:14 a.m. | Ben Prystawski, Paul Thibodeau, Noah Goodman

cs.CL updates on arXiv.org arxiv.org

Probabilistic models of language understanding are interpretable and
structured, for instance models of metaphor understanding describe inference
about latent topics and features. However, these models are manually designed
for a specific task. Large language models (LLMs) can perform many tasks
through in-context learning, but they lack the clear structure of probabilistic
models. In this paper, we use chain-of-thought prompts to introduce structures
from probabilistic models into LLMs. These prompts lead the model to infer
latent variables and reason about their …

arxiv language language models large language models understanding

More from arxiv.org / cs.CL updates on arXiv.org

Postdoctoral Fellow: ML for autonomous materials discovery

@ Lawrence Berkeley National Lab | Berkeley, CA

Research Scientists

@ ODU Research Foundation | Norfolk, Virginia

Embedded Systems Engineer (Robotics)

@ Neo Cybernetica | Bedford, New Hampshire

2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences

@ Lawrence Berkeley National Lab | San Francisco, CA

Senior Manager Data Scientist

@ NAV | Remote, US

Senior AI Research Scientist

@ Earth Species Project | Remote anywhere

Research Fellow- Center for Security and Emerging Technology (Multiple Opportunities)

@ University of California Davis | Washington, DC

Staff Fellow - Data Scientist

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Staff Fellow - Senior Data Engineer

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Research Engineer - VFX, Neural Compositing

@ Flawless | Los Angeles, California, United States

[Job-TB] Senior Data Engineer

@ CI&T | Brazil

Data Analytics Engineer

@ The Fork | Paris, France