Difference between revisions of "Timeline of large language models"
From Timelines
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| 2021 || May || || Google anounces chatbot LaMDA, but doesn't release it publicly. | | 2021 || May || || Google anounces chatbot LaMDA, but doesn't release it publicly. | ||
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| + | | 2022 || April || || OpenAI reveals DALL-E 2. | ||
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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> | | 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> | ||
Revision as of 21:00, 6 March 2023
This is a timeline of FIXME.
Contents
Sample questions
The following are some interesting questions that can be answered by reading this timeline:
Big picture
| Time period | Development summary | More details |
|---|
Full timeline
| Year | Month and date | Event type | Details |
|---|---|---|---|
| 2021 | May | Google anounces chatbot LaMDA, but doesn't release it publicly. | |
| 2022 | April | OpenAI reveals DALL-E 2. | |
| 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] |
Meta information on the timeline
How the timeline was built
The initial version of the timeline was written by FIXME.
Funding information for this timeline is available.
Feedback and comments
Feedback for the timeline can be provided at the following places:
- FIXME
What the timeline is still missing
Timeline update strategy
See also
External links
References
- ↑ Yuan, Yang (2023). "Succinct Representations for Concepts". doi:10.48550/arXiv.2303.00446.
