[Submitted on 2 Apr 2023]
[View PDF](/pdf/2304.00612)
[HTML (experimental)](https://arxiv.org/html/2304.00612v1)
Abstract:The widespread public deployment of large language models (LLMs) in recent months has prompted a wave of new attention and engagement from advocates, policymakers, and scholars from many fields. This attention is a timely response to the many urgent questions that this technology raises, but it can sometimes miss important considerations. This paper surveys the evidence for eight potentially surprising such points:
-
LLMs predictably get more capable with increasing investment, even without targeted innovation.
-
Many important LLM behaviors emerge unpredictably as a byproduct of increasing investment.
-
LLMs often appear to learn and use representations of the outside world.
-
There are no reliable techniques for steering the behavior of LLMs.
-
Experts are not yet able to interpret the inner workings of LLMs.
-
Human performance on a task isn't an upper bound on LLM performance.
-
LLMs need not express the values of their creators nor the values encoded in web text.
-
Brief interactions with LLMs are often misleading.
References & Citations
...
Bibliographic Explorer
(What is the Explorer?) Connected Papers
(What is Connected Papers?) Litmaps
(What is Litmaps?) scite Smart Citations
(What are Smart Citations?)# Code, Data and Media Associated with this Article alphaXiv
(What is alphaXiv?) CatalyzeX Code Finder for Papers
(What is CatalyzeX?) DagsHub
(What is DagsHub?) Gotit.pub
(What is GotitPub?) Hugging Face
(What is Huggingface?) ScienceCast
(What is ScienceCast?)# Demos Influence Flower
(What are Influence Flowers?) CORE Recommender
(What is CORE?)# arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.