# Foundation Models for Natural Language Processing -- Pre-trained Language Models Integrating Media

arXiv: https://arxiv.org/abs/2302.08575 · HTML: https://mlsanity.com/paper/2302.08575/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2023.

## Built on by (most cited first, 8 of 8)

- [V2A-Mapper: A Lightweight Solution for Vision-to-Audio Generation by Connecting Foundation Models](https://mlsanity.com/paper/2308.09300/) (2023)
- [Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?](https://mlsanity.com/paper/2401.12902/) (2024)
- [The Machine Psychology of Cooperation: Can GPT models operationalise prompts for altruism, cooperation, competitiveness and selfishness in economic games?](https://mlsanity.com/paper/2305.07970/) (2023)
- [GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation](https://mlsanity.com/paper/2305.18997/) (2023)
- [Foundation Models for Structural Health Monitoring](https://mlsanity.com/paper/2404.02944/) (2024)
- [Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models](https://mlsanity.com/paper/2410.12341/) (2024)
- [Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision](https://mlsanity.com/paper/2311.02333/) (2023)
- [On Next-Token Prediction in LLMs: How End Goals Determine the Consistency of Decoding Algorithms](https://mlsanity.com/paper/2505.11183/) (2025)

## Abstract (arXiv)

This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.