Talking about Large Language Models

Author:

Shanahan Murray1

Affiliation:

1. Imperial College London, U.K

Abstract

Interacting with a contemporary LLM-based conversational agent can create an illusion of being in the presence of a thinking creature. Yet, in their very nature, such systems are fundamentally not like us.

Publisher

Association for Computing Machinery (ACM)

Reference41 articles.

1. Ahn M. et al. Do as I can not as I say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.01691. (2022).

2. Alayrac, J.-B. et al. Flamingo: A visual language model for few-shot learning. In Advances in Neural Information Processing Systems (2022).

3. Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data

4. Bender, E., Gebru, T., McMillan-Major, A., and Shmitchell, S. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conf. on Fairness, Accountability, and Transparency, 610--623.

5. Language models are few-shot learners;Brown T.;Advances in Neural Information Processing Systems,2020

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