Difference between revisions of "Timeline of large language models"
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| 2023 || January 5 || || A paper discusses the concern about the potential of LLMs to influence, modify, and manipulate user preferences adversarially. As these models become more proficient in deducing user preferences and offering tailored assistance, their lack of interpretability in adversarial settings is a major concern. The paper examines existing literature on adversarial behavior in user preferences and provides red teaming samples for dialogue models like ChatGPT and GODEL. It also probes the attention mechanism in these models for non-adversarial and adversarial settings.<ref>{{cite journal |last1=Subhash |first1=Varshini |title=Can Large Language Models Change User Preference Adversarially? |journal=arXiv:2302.10291 [cs] |date=5 January 2023 |doi=10.48550/arXiv.2302.10291 |url=https://arxiv.org/abs/2302.10291}}</ref> | | 2023 || January 5 || || A paper discusses the concern about the potential of LLMs to influence, modify, and manipulate user preferences adversarially. As these models become more proficient in deducing user preferences and offering tailored assistance, their lack of interpretability in adversarial settings is a major concern. The paper examines existing literature on adversarial behavior in user preferences and provides red teaming samples for dialogue models like ChatGPT and GODEL. It also probes the attention mechanism in these models for non-adversarial and adversarial settings.<ref>{{cite journal |last1=Subhash |first1=Varshini |title=Can Large Language Models Change User Preference Adversarially? |journal=arXiv:2302.10291 [cs] |date=5 January 2023 |doi=10.48550/arXiv.2302.10291 |url=https://arxiv.org/abs/2302.10291}}</ref> | ||
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| + | | 2023 || February 17 || Study || A paper surveys the state of the art of hybrid language models architectures and strategies for complex question-answering (QA, CQA, CPS). While very large language models are good at leveraging public data on standard problems, they may require specific architecture, knowledge, skills, tasks, methods, sensitive data, performance, human approval, and versatile feedback to tackle more specific complex questions or problems. The paper identifies the key elements used with LLMs to solve complex questions or problems and discusses challenges associated with complex QA. The paper also reviews current solutions and promising strategies, using elements such as hybrid LLM architectures, human-in-the-loop reinforcement learning, prompting adaptation, neuro-symbolic and structured knowledge grounding, program synthesis, and others.<ref>{{cite journal |last1=Daull |first1=Xavier |last2=Bellot |first2=Patrice |last3=Bruno |first3=Emmanuel |last4=Martin |first4=Vincent |last5=Murisasco |first5=Elisabeth |title=Complex QA and language models hybrid architectures, Survey |journal=arXiv:2302.09051 [cs] |date=17 February 2023 |doi=10.48550/arXiv.2302.09051 |url=https://arxiv.org/abs/2302.09051}}</ref> | ||
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| 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 {{w|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.<ref>{{cite journal |last1=White |first1=Jules |last2=Fu |first2=Quchen |last3=Hays |first3=Sam |last4=Sandborn |first4=Michael |last5=Olea |first5=Carlos |last6=Gilbert |first6=Henry |last7=Elnashar |first7=Ashraf |last8=Spencer-Smith |first8=Jesse |last9=Schmidt |first9=Douglas C. |title=A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT |journal=arXiv:2302.11382 [cs] |date=21 February 2023 |doi=10.48550/arXiv.2302.11382 |url=https://arxiv.org/abs/2302.11382}}</ref> | | 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 {{w|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.<ref>{{cite journal |last1=White |first1=Jules |last2=Fu |first2=Quchen |last3=Hays |first3=Sam |last4=Sandborn |first4=Michael |last5=Olea |first5=Carlos |last6=Gilbert |first6=Henry |last7=Elnashar |first7=Ashraf |last8=Spencer-Smith |first8=Jesse |last9=Schmidt |first9=Douglas C. |title=A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT |journal=arXiv:2302.11382 [cs] |date=21 February 2023 |doi=10.48550/arXiv.2302.11382 |url=https://arxiv.org/abs/2302.11382}}</ref> | ||
Revision as of 13:12, 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 | January 5 | A paper discusses the concern about the potential of LLMs to influence, modify, and manipulate user preferences adversarially. As these models become more proficient in deducing user preferences and offering tailored assistance, their lack of interpretability in adversarial settings is a major concern. The paper examines existing literature on adversarial behavior in user preferences and provides red teaming samples for dialogue models like ChatGPT and GODEL. It also probes the attention mechanism in these models for non-adversarial and adversarial settings.[1] | |
| 2023 | February 17 | Study | A paper surveys the state of the art of hybrid language models architectures and strategies for complex question-answering (QA, CQA, CPS). While very large language models are good at leveraging public data on standard problems, they may require specific architecture, knowledge, skills, tasks, methods, sensitive data, performance, human approval, and versatile feedback to tackle more specific complex questions or problems. The paper identifies the key elements used with LLMs to solve complex questions or problems and discusses challenges associated with complex QA. The paper also reviews current solutions and promising strategies, using elements such as hybrid LLM architectures, human-in-the-loop reinforcement learning, prompting adaptation, neuro-symbolic and structured knowledge grounding, program synthesis, and others.[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.[3] |
| 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.[4] |
| 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.[5] |
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References
- ↑ Subhash, Varshini (5 January 2023). "Can Large Language Models Change User Preference Adversarially?". arXiv:2302.10291 [cs]. doi:10.48550/arXiv.2302.10291.
- ↑ Daull, Xavier; Bellot, Patrice; Bruno, Emmanuel; Martin, Vincent; Murisasco, Elisabeth (17 February 2023). "Complex QA and language models hybrid architectures, Survey". arXiv:2302.09051 [cs]. doi:10.48550/arXiv.2302.09051.
- ↑ 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.
