Difference between revisions of "Timeline of large language models"
From Timelines
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| 2022 || April || || OpenAI reveals DALL-E 2. | | 2022 || April || || OpenAI reveals DALL-E 2. | ||
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| + | | 2023 || February 24 || Study || A paper proposes a system called LLM-Augmenter that improves large language models by using external knowledge and automated feedback. The system adds plug-and-play modules to a black-box LLM to ground responses in external knowledge and iteratively improve responses using feedback generated by utility functions. The system is validated on task-oriented dialog and open-domain question answering, showing a significant reduction in hallucinations without sacrificing fluency and informativeness. The source code and models are publicly available.<ref>{{cite journal |last1=Peng |first1=Baolin |last2=Galley |first2=Michel |last3=He |first3=Pengcheng |last4=Cheng |first4=Hao |last5=Xie |first5=Yujia |last6=Hu |first6=Yu |last7=Huang |first7=Qiuyuan |last8=Liden |first8=Lars |last9=Yu |first9=Zhou |last10=Chen |first10=Weizhu |last11=Gao |first11=Jianfeng |title=Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback |journal=arXiv:2302.12813 [cs] |date=1 March 2023 |doi=10.48550/arXiv.2302.12813 |url=https://arxiv.org/abs/2302.12813}}</ref> | ||
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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 11:14, 7 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 | February 24 | Study | A paper proposes a system called LLM-Augmenter that improves large language models by using external knowledge and automated feedback. The system adds plug-and-play modules to a black-box LLM to ground responses in external knowledge and iteratively improve responses using feedback generated by utility functions. The system is validated on task-oriented dialog and open-domain question answering, showing a significant reduction in hallucinations without sacrificing fluency and informativeness. The source code and models are publicly available.[1] |
| 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.[2] |
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The initial version of the timeline was written by FIXME.
Funding information for this timeline is available.
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Feedback for the timeline can be provided at the following places:
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What the timeline is still missing
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See also
External links
References
- ↑ Peng, Baolin; Galley, Michel; He, Pengcheng; Cheng, Hao; Xie, Yujia; Hu, Yu; Huang, Qiuyuan; Liden, Lars; Yu, Zhou; Chen, Weizhu; Gao, Jianfeng (1 March 2023). "Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback". arXiv:2302.12813 [cs]. doi:10.48550/arXiv.2302.12813.
- ↑ Yuan, Yang (2023). "Succinct Representations for Concepts". doi:10.48550/arXiv.2303.00446.
