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	March 29	Large-scale transformer language model			A paper investigates the optimal model size and number of tokens for training a transformer language model under a given compute budget. The researchers find that current large language models are significantly undertrained, and the model size and the number of training tokens should be scaled equally for compute-optimal training. They test this hypothesis by training a predicted compute-optimal model, Chinchilla, that uses the same compute budget as Gopher but with 70B parameters and 4x more data. Chinchilla outperforms Gopher, GPT-3, Jurassic-1, and Megatron-Turing NLG on a range of downstream evaluation tasks and reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, more than a 7% improvement over Gopher.
2020	May 28	Large-scale language model			A paper discusses the use of language models in few-shot learning, where a model is trained on a large corpus of text and then fine-tuned for a specific task. The authors demonstrate that scaling up language models greatly improves task-agnostic, few-shot performance. They trained GPT-3, a language model with 175 billion parameters, and tested its performance in the few-shot setting. GPT-3 achieved strong performance on many NLP tasks, including translation, question-answering, and cloze tasks, as well as tasks that require on-the-fly reasoning or domain adaptation. However, the authors also identify some datasets where GPT-3's few-shot learning struggles, as well as methodological issues related to training on large web corpora. The paper also discusses the broader societal impacts of this finding and of GPT-3 in general.
2020	July	Neural text generation model			A paper discusses the limitations of neural text generation models in open-ended tasks like language modeling and story generation, due to the standard likelihood training and approximate decoding objectives. The authors specifically analyze these limitations for abstractive document summarization and find that such models tend to hallucinate content that is unfaithful to the input document. The paper presents the results of a human evaluation of several neural abstractive summarization systems, highlighting the substantial amount of hallucinated content in all model-generated summaries. However, the authors also show that pretrained models perform better in terms of generating faithful and factual summaries, as evaluated by humans. They propose that textual entailment measures may be a better evaluation metric for faithfulness than standard metrics, leading to better training and decoding criteria.
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	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.
2023	February 9				A paper presents a collaborative design framework that combines interactive evolution and LLMs to simulate the human design process. The framework uses interactive evolution to exploit user feedback and LLMs for a complex creative task of recombining and varying ideas. The process begins with a brief and a set of candidate designs, generated by a language model or proposed by users. Users provide feedback to an interactive genetic algorithm that selects, recombines, and mutates the most promising designs. The framework was evaluated on three game design tasks with human designers collaborating remotely.
2023	February 14		Research		A paper presents a framework called ChatCAD, which integrates LLMs with computer-aided diagnosis (CAD) networks for medical images. ChatCAD uses LLMs to enhance the output of multiple CAD networks by summarizing and reorganizing the information presented in natural language text format. This approach merges the strengths of LLMs' medical domain knowledge and logical reasoning with the vision understanding capability of existing medical-image CAD models. The goal is to create a more user-friendly and understandable system for patients compared to conventional CAD systems. The paper suggests that LLMs can also be used to improve the performance of vision-based medical-image CAD models in the future.
2023	February 17	Hybrid language model		Research	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.
2023	February 21			Research	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.
2023	February 24			Research	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.
2023	February 27				A paper proposes a method called "rectification" for reducing the risk of LLMs generating toxic discourses. The method is based on the probability that the finished discourse will be considered toxic, and advises against token selections proportional to this probability. The approach utilizes a separate but smaller model for detoxification and does not require access to the internal representations of the LLM. The method significantly improves the generated discourse compared to base LLMs and other techniques in terms of both language and detoxification performance, and can be applied to diverse LLMs that share the same vocabulary.
2023	February 27			Research	A paper discusses the use of open source code to train large language models (LLMs) and the potential security, privacy, and licensing implications of this practice. LLMs for code are commonly trained on large unsanitized corpora of source code scraped from the internet, leading to the memorization and verbatim emission of content by the models. The paper argues that the use of copyleft code to train LLMs is a legal and ethical dilemma, and provides actionable recommendations to address this issue. Overall, the paper highlights the importance of considering the implications of using open source code in training LLMs.
2023	March 6			Research	A paper proposes a perspective on prompts for LLMs that distinguishes between diegetic and non-diegetic prompts, and studies how users write with LLMs using different user interfaces. The results show that when the interface offered multiple suggestions and provided an option for non-diegetic prompting, participants preferred choosing from multiple suggestions over controlling them via non-diegetic prompts. When participants provided non-diegetic prompts it was to ask for inspiration, topics or facts. Single suggestions in particular were guided both with diegetic and non-diegetic information. The paper informs human-AI interaction with generative models by revealing that writing non-diegetic prompts requires effort, people combine diegetic and non-diegetic prompting, and they use their draft and suggestion timing to strategically guide LLMs.
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.
2023	May 21				Rodney Brooks, a robotics researcher and AI expert, argues that large language models like OpenAI's ChatGPT are not as intelligent as people believe and are far from being able to compete with humans on an intellectual level. Brooks highlights that these models lack an underlying understanding of the world and merely exhibit correlations in language. Current language models can sound like they understand, but they lack the ability to logically infer meaning, leading to potential misinterpretations. Brooks emphasizes that these models are good at generating answers that sound right but may not be accurate. He shares his experience of relying on large language models for coding tasks and finding that they often provide confidently wrong answers. Brooks concludes that while future iterations of AI may bring interesting advancements, they are unlikely to achieve artificial general intelligence (AGI).