Large Language Models (LLMs) have gained significant attention in the field of natural language processing due to their excellent generalization capabilities. This has led to the development of numerous models that propose various new architectures and training strategies to outperform baselines. The closed-source nature of GPT-3 has also prompted the development of open-source alternatives operating at a similar scale and trained on extensive web-based datasets. One approach that has shown superior performance compared to GPT-3 is instruction fine-tuning, which involves incorporating additional prompts or instructions during the fine-tuning phase to guide the output of LLMs. This allows users to have more control over the outputs and improves performance on various benchmarks. Parameter-efficient fine-tuning is another strategy that updates only a small number of parameters while maintaining good performance, minimizing the impact on the overall model's performance. While there are existing survey papers that provide an overview of augmented techniques and cover architectures, fine-tuning, emergent abilities, and usability of LLMs, they often lack detailed discussions on individual models. In contrast, this comprehensive survey aims to provide an in-depth analysis of individual LLMs by discussing fine-grained details. This paper not only analyzes LLM architectures and their categorization but also provides a thorough understanding of the basics behind LLMs for readers who may not be familiar with them. It delves into historical developments, training datasets, and other granular aspects of LLMs that have been overlooked in previous surveys. Overall, this survey paper fills a gap in existing literature by providing a comprehensive analysis and detailed discussions on various aspects of LLMs. It serves as a valuable resource for researchers and practitioners interested in understanding and developing advanced LLMs. Additionally, it highlights the need for regular updates as new advancements continue to emerge in this rapidly evolving field.
Large Language Models (LLMs) are computer programs that are really good at understanding and using human language. Many different models have been created to be even better than the basic ones and come up with new ways of working. Some alternatives to a model called GPT-3 have been made by people and trained on lots of information from the internet. Instruction fine-tuning is a way to make LLMs even better by giving them extra instructions during their training. Parameter-efficient fine-tuning is another way to improve LLMs by only changing a few things instead of everything. This survey paper is special because it talks about each LLM in detail, including how they work and what they're used for. It also fills in gaps in other papers by talking about things that were missed before. This paper is important for researchers and people who want to learn more about advanced LLMs. It's always good to keep updating our knowledge as new things happen in this field."
A Comprehensive Survey of Large Language Models
In recent years, natural language processing (NLP) has seen a surge in the development of large language models (LLMs). These models are capable of generalizing well and have been used to outperform baselines. GPT-3 is an example of such a model, though it is closed source and thus has prompted the development of open-source alternatives that operate at similar scales.
This paper provides an in-depth analysis into LLMs by discussing fine-grained details. It delves into historical developments, training datasets, and other aspects that have been overlooked in previous surveys. This comprehensive survey aims to provide readers with a thorough understanding of the basics behind LLMs while also analyzing architectures and categorization.
Augmented Techniques
Instruction fine-tuning is one approach that has shown superior performance compared to GPT-3. This technique involves incorporating additional prompts or instructions during the fine-tuning phase to guide the output of LLMs, allowing users more control over outputs and improving performance on various benchmarks. Parameter efficient fine tuning is another strategy which updates only a small number of parameters while maintaining good performance without significantly impacting overall model performance.
Usability & Emergent Abilities
This survey paper not only covers architectures, fine tuning techniques but also provides insights into emergent abilities and usability for those who may not be familiar with them. It highlights the need for regular updates as new advancements continue to emerge in this rapidly evolving field as well as serves as a valuable resource for researchers interested in developing advanced LLMs.
Conclusion
Overall, this comprehensive survey fills a gap in existing literature by providing an in depth analysis and detailed discussions on various aspects related to large language models (LLM). It serves as an invaluable resource for practitioners interested in understanding these complex systems better so they can develop more advanced applications using them going forward.
Large Language Models (LLMs) have shown excellent generalization capabilities that have led to the development of numerous models. These models propose various new architectures, tweaking existing architectures with refined training strategies, increasing context length, using high-quality training data, and increasing training time to outperform baselines. Analyzing new developments is crucial for identifying changes that enhance training stability and improve generalization in LLMs. This survey paper comprehensively analyses the LLMs architectures and their categorization, training strategies, training datasets, and performance evaluations and discusses future research directions. Moreover, the paper also discusses the basic building blocks and concepts behind LLMs, followed by a complete overview of LLMs, including their important features and functions. Finally, the paper summarizes significant findings from LLM research and consolidates essential architectural and training strategies for developing advanced LLMs. Given the continuous advancements in LLMs, we intend to regularly update this paper by incorporating new sections and featuring the latest LLM models.
