Timeline of large language models
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| 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 21 | Study | A paper presents a catalog of prompt engineering techniques in pattern form that have been applied successfully to solve common problems when conversing with large language models (LLMs), such as ChatGPT. Prompt patterns are reusable solutions to common problems faced when working with LLMs that can customize the outputs and interactions with an LLM. The paper provides a framework for documenting patterns for structuring prompts to solve a range of problems and presents a catalog of patterns that have been applied successfully to improve the outputs of LLM conversations. It also explains how prompts can be built from multiple patterns and illustrates prompt patterns that benefit from combination with other prompt patterns. The paper contributes to research on prompt engineering that applies LLMs to automate software development tasks.[1] |
| 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.[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.[3] |
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References
- ↑ White, Jules; Fu, Quchen; Hays, Sam; Sandborn, Michael; Olea, Carlos; Gilbert, Henry; Elnashar, Ashraf; Spencer-Smith, Jesse; Schmidt, Douglas C. (21 February 2023). "A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT". arXiv:2302.11382 [cs]. doi:10.48550/arXiv.2302.11382.
- ↑ 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.
