Talk:Timeline of large language models

Sample questions

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

Concepts without articles on Wikipedia

• BioNeMo

Year	Month and date	Model name	Number of parameters	Event type	Details
2022	April 12	Reinforcement learning-based language model			A paper describes a method for training language models to act as helpful and harmless assistants using reinforcement learning from human feedback. The authors demonstrate that this alignment training improves performance on almost all natural language processing evaluations and is compatible with training for specialized skills such as python coding and summarization. They explore an iterated online mode of training and investigate the robustness of the approach, identifying a linear relationship between the RL reward and the square root of the Kullback–Leibler divergence between the policy and its initialization. The authors also perform peripheral analyses and provide samples from their models using prompts from recent related work.
2022	June 2				OpenAI publishes a blog post on the development of best practices for organizations developing or deploying large language models. The principles include prohibiting misuse of language models, mitigating unintentional harm by evaluating models, minimizing sources of bias, and collaborating with stakeholders. These practices are meant to mitigate the risks of language models and achieve their full potential to augment human capabilities. The authors express hope that other organizations will adopt these principles and advance public discussion on language model development and deployment. The support from other organizations shows the growing social concern over the safety of LLMs.
2022	September		Competition		Nvidia announces the launch of its BioNeMo LLM service to help researchers build new artificial intelligence models for biology.
2023	Marh 14	Medical language model			Google shares health AI updates including progress on their Medical PaLM 2, expert-level medical language model (LLM) research which demonstrated consistently expert-level performance on medical exam questions, scoring 85%. The company has partnered with Jacaranda Health and Chang Gung Memorial Hospital to build AI models that can help simplify acquiring and interpreting ultrasound images to identify important information like gestational age in expecting mothers and early detection of breast cancer. They're also partners with Mayo Clinic with the purpose to extend the reach of their AI model, with the goal of helping more patients receive radiotherapy treatment sooner. Additionally, Google works with partners on the ground to bring their research on tuberculosis (TB) AI-powered chest x-ray screening into the care setting.