Michel Bréal developed the concept of semantics in 1883, which laid the foundation for the history of large language models. He studied language organization, evolution, and word connections.

ELIZA, a language model program, was developed by Joseph Weizenbaum at MIT in 1966. It used a simple set of rules to mimic human conversation, representing an early example of language modeling technology.

SHRDLU was a system developed to understand and respond to commands in a restricted world of geometric shapes. It was one of the first programs to demonstrate natural language understanding, but its capabilities were limited to the specific domain it was designed for.

The concept of large language models, requiring complex training with massive amounts of data, was initiated around 1989, leading to significant advancements in natural language processing.

The advent of LSTM networks resulted in deeper and more complex neural networks that could handle greater amounts of data, contributing to the advancement of large language models.

Stanford’s CoreNLP suite allowed developers to perform sentiment analysis and named entity recognition, marking a significant stage of growth in the development of large language models.

Google Brain introduced advanced features such as word embeddings, which enabled NLP systems to gain a clearer understanding of context, signifying a significant turning point in the development of large language models.

The concept of Attention in the context of transformers has revolutionized natural language processing.

The Ul2 model, based on the concept of Attention, has been a major development in large language models.

The Gpt model, utilizing the concept of Attention, has had a profound impact on natural language processing.

On June 11, 2018, there were efforts to enhance language understanding through generative pre-training. This likely contributed to advancements in natural language processing and machine learning.

The Lamda model, leveraging the concept of Attention, has significantly improved large language models.

BERT stands for Bidirectional Encoder Representations from Transformers. It is a pre-training technique for natural language processing based on deep bidirectional transformers.

The MegatronLm model, built on the concept of Attention, has made significant contributions to large language models.

On February 14, 2019, the concept of Language Models as Unsupervised Multitask Learners is introduced.

A method for generating long sequences with sparse transformers, known as LSST, is presented to address the challenge of processing lengthy inputs in language models.

The Whisper model, incorporating Attention, has made notable advancements in large language models.

Megatron-LM is a method for training multi-billion parameter language models using model parallelism.

The T5 model, incorporating Attention, has significantly advanced the capabilities of large language models.

T5, a state-of-the-art LLM for code, was introduced on October 23, 2019.

A method called FlashAttention was introduced to provide fast and memory-efficient exact attention with IO-awareness.

The Longformer, a long-document transformer, is introduced to effectively process and analyze lengthy documents, offering a solution for handling extensive textual data.

On May 28, 2020, the concept of Language Models as Few-Shot Learners is introduced.

Introduction of denoising diffusion probabilistic models.

The event involves the training of CLIP, a model for learning transferable visual models from natural language supervision.

LaMDA is a conversational technology that represents a significant advancement in natural language processing. It enables more sophisticated and contextually relevant conversations compared to traditional models.

On June 4, 2021, the GPT-J-6B, a JAX-based transformer, was likely introduced or discussed. This transformer model is significant in the field of large language models and natural language processing.

OpenAI Codex is a large language model designed for code understanding and generation.

On September 3, 2021, CodeT5 was launched, offering identifier-aware unified pre-trained encoder-decoder models for code understanding and generation.

Multitask Prompted Training (MTF) enables zero-shot task generalization, allowing models to perform tasks they were not explicitly trained for. It represents a significant advancement in the field of language models and artificial intelligence.

OpenAI announced WebGPT in December 2021, expanding the capabilities of large language models in web-related tasks and applications.

A project or research related to generating high-resolution images using latent diffusion models.

A project or research aimed at training language models to understand and follow instructions based on human feedback. This involves developing AI models that can learn from human input to accurately interpret and execute instructions.

CoT is involved in adding conditional control to text-to-image diffusion models.

The Pathways Language Model (PaLM) is scaled to 540 billion parameters for achieving breakthrough performance.

A project or research conducted on training an assistant using reinforcement learning and human feedback to ensure it is helpful and harmless. The focus is on developing an AI model that can learn from human input to provide assistance without causing harm.

On April 13, 2022, DALL-E 2 was released, building upon the capabilities of the original DALL-E.

UL2 is an event focused on bringing together different language learning methods and approaches. It aims to create a unified paradigm for language learning.

A project or research focused on creating text-to-image diffusion models with deep language understanding to generate photorealistic images based on textual input. This involves advanced AI technology that can interpret and generate images from text.

The release of a new open source version of Flan 20B with UL2 represents a significant development in the field of language models. It offers enhanced features and accessibility for language model enthusiasts and developers.

The StableLM model is achieving stable diffusion in the AI community.

Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering

Flamingo is a visual language model designed for few-shot learning, allowing it to understand and learn from a small amount of data. It aims to improve the performance of language models in tasks requiring minimal training examples.

BLOOM is introduced as a 176 billion parameter open-access multilingual language model.

The Stack event involves the release of 3 TB of permissively licensed source code.

GPT-JT, powered by open-source AI, is released on November 29, 2022.

The event involves the introduction of ChatGPT, a model designed for crosslingual generalization through multitask finetuning.

The event 'Self-Instruct' took place on this date. No further details were provided.

The event AudioLDM took place on January 29, 2023. It focused on the development of Text-to-Audio generation using Latent Diffusion Models.

BLIP-2 refers to Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models. It is a significant advancement in AI that involves pre-training models using both language and image data.

ControlNet is focused on evaluating large language models trained on code.

Introduction of LLaMA as an open and efficient foundation language model.

This event involves the scaling of instruction-finetuned language models, particularly Flan-UL2, to enhance their capabilities and performance. It signifies advancements in language model development and application.

Efforts are made to train language models to follow instructions with human feedback, aiming to improve their understanding and responsiveness.

Together Computer released OpenChatKit, a design for large language models used for new chatbots, as an open-source project on March 10, 2023.

The paper titled 'Alpaca' introduces a robust and reproducible instruction-following model.

On March 15, 2023, GPT-4 was released, representing the next version of the Generative Pre-trained Transformer (GPT) model.

The event Bard took place on March 21, 2023. The specific details of the event were not mentioned.

Early experiments with GPT-4 are leading to the emergence of Artificial General Intelligence.

The LLaMA-Adapter is introduced as an efficient method for fine-tuning language models with zero-init attention, aiming to improve the performance of language models.

Vicuna is an open-source chatbot that has impressed with its high quality conversation capabilities, achieving 90% ChatGPT quality. It showcases advancements in chatbot technology and open-source development.

Koala is a dialogue model designed specifically for academic research purposes. It aims to facilitate conversations and interactions related to scholarly topics.

On April 7, 2023, Generative Agents, interactive simulacra of human behavior, were likely a topic of discussion or development. These agents are designed to simulate human behavior in an interactive manner.

The event marks the introduction of the World's First Truly Open Instruction-Tuned Large Language Model (LLM) called Dolly 2.0, which aims to democratize the magic of ChatGPT with open models.

On April 15, 2023, OpenAssistant introduced OpenAssistant Conversations, aiming to democratize large language model alignment.

MiniGPT-4 is a development focused on improving the understanding of vision and language using advanced large language models. It represents a significant advancement in the field of artificial intelligence.

The event VideoLDM is not specified in the input. However, it could be related to video content generation using Large Language Models, showcasing advancements in multimedia content creation.

StableLM model continues to make significant progress in the field of language modeling.

On April 26, 2023, a survey titled 'Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond' was conducted. The survey focused on exploring the practical applications of large language models like ChatGPT and their impact.

The blog post 'AmbiEnt' raises concerns about the ability of language models to accurately represent ambiguity.

Stability AI releases DeepFloyd IF, a powerful text-to-image model that can intelligently integrate text into images.

OpenLLaMA is a large language model alignment project aimed at democratizing the development and use of large language models.

StarCoderBase was introduced as a state-of-the-art LLM for code on May 4, 2023.

MPT-7B is a new standard for open-source, commercially usable Large Language Models (LLMs). It aims to set a benchmark for LLMs that can be used for various tasks and applications.

The technical report for PaLM 2 is released, providing detailed information about the model.

On May 20, 2023, CodeT5+ was introduced, likely as an enhanced version of the existing CodeT5 model.

RWKV: Reinventing RNNs for the Transformer Era

This event highlights the concept of Direct Preference Optimization (DPO) and its implications on language models, suggesting that language models may function as reward models in secret. It represents a crucial development in the understanding of language model behavior.

The event marks the introduction of the world's first truly open instruction-tuned Large Language Model (LLM), named Free Dolly. It signifies a significant advancement in language model technology.

Stable Diffusion XL 0.9 was released, aiming to improve Latent Diffusion Models for High-Resolution Image Synthesis.

A 7B Large Language Model trained on 8K input sequence length was introduced.

Introduction of Llama 2 as an open foundation with fine-tuned chat models.

The release of SDXL 1.0 was announced.

MetaGPT is a programming framework designed for multi-agent collaboration. It aims to enhance the capabilities of artificial intelligence through advanced programming techniques.

On August 24, 2023, Code Llama introduced the Open Foundation Models for Code, aiming to enhance code understanding and generation.

The technical report for Textbooks Are All You Need II with phi-1.5 was released on September 11, 2023.

On September 27, 2023, Mistral 7B, a significant technological advancement, was introduced.

In October 19, 2023, DALL-E 3 was introduced, further advancing the text-conditional image generation with CLIP latents.

An event showcasing robust speech recognition achieved through large-scale weak supervision.

Gemini, a family of highly capable multimodal models, was introduced to address various tasks effectively.

An event called Mixtral of experts is scheduled to take place on December 11, 2023.

On December 12, 2023, the surprising power of small language models was discussed in the Phi-2 event.

Viktor Garske made the last update on this date regarding AI/ML/LLM/Transformer Models timeline and list.

Large language models (LLMs) are artificial neural networks that have rapidly transitioned from recent development to widespread use within a few years. They have played a crucial role in the advancement of ChatGPT, a more intelligent form of artificial intelligence achieved through the combination of generative AI with large language models.

A guide on how to perform supervised fine-tuning to customize Large Language Models for specific applications.

An article explaining the distinction between labeled and unlabeled data in the context of data labeling and machine learning.

LLM-based GPT products are capable of learning and communicating like humans, posing potential threats to job security across industries and raising concerns about the obsoletion of traditional academic essays. However, there is also excitement about the limitless potential and numerous opportunities offered by this innovative technology.

An improvement in Large Language Models (LLM) output was achieved by combining RAG and fine-tuning techniques.

Large Language Models (LLMs) are anticipated to further expand their capabilities in handling business applications, particularly in terms of translating content across different contexts. This expansion is expected to make LLMs more accessible to business users with varying levels of technical expertise.

Most LLMs can be used for sentiment analysis to help users better understand the intent of a piece of content or a particular response.