Difference between revisions of "Timeline of large language models"

Revision as of 12:57, 6 March 2023

This is a timeline of FIXME.

The following are some interesting questions that can be answered by reading this timeline:

Time period	Development summary	More details

Year	Month and date	Event type	Details
2023	March 1	Study	A paper introduces a method to train language models like ChatGPT to understand concepts precisely using succinct representations based on category theory. The representations provide concept-wise invariance properties and a new learning algorithm that can accurately learn complex concepts or fix misconceptions. The approach also allows for the generation of a hierarchical decomposition of the representations, which can be manually verified by examining each part individually.^[1]

The initial version of the timeline was written by FIXME.

Feedback for the timeline can be provided at the following places:

↑ Yuan, Yang (2023). "Succinct Representations for Concepts". doi:10.48550/arXiv.2303.00446.

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+| 2023 || March 1 || Study || A paper introduces a method to train language models like ChatGPT to understand concepts precisely using succinct representations based on category theory. The representations provide concept-wise invariance properties and a new learning algorithm that can accurately learn complex concepts or fix misconceptions. The approach also allows for the generation of a hierarchical decomposition of the representations, which can be manually verified by examining each part individually.<ref>{{cite journal |last1=Yuan |first1=Yang |title=Succinct Representations for Concepts |date=2023 |doi=10.48550/arXiv.2303.00446}}</ref>
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