2023 AComprehensiveOverviewofLargeLa
- (Naveed et al., 2023) ⇒ Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Nick Barnes, and Ajmal Mian. (2023). “A Comprehensive Overview of Large Language Models.” In: arXiv preprint arXiv:2307.06435. doi:10.48550/arXiv.2307.06435
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- The article outlines the significant advancements in Large Language Models (LLMs), highlighting their transformative role in natural language processing tasks. It emphasizes the evolution from statistical and neural language modeling towards the development of pre-trained language models (PLMs) and subsequently LLMs, which are characterized by their vast parameter sizes and extensive training datasets.
- The article categorizes LLMs into several types, including general-purpose models like GPT-3, domain-specific models such as Codex for coding, and models fine-tuned for specific tasks or languages. This categorization demonstrates the versatility and wide-ranging applicability of LLMs in various fields.
- The article delves into the methods and algorithms that underpin LLMs, with a special focus on tokenization, encoding positions, attention mechanisms, and activation functions. It discusses the significance of these components in improving the efficiency and effectiveness of LLMs.
- The article explores various implementations and systems associated with LLMs, including distributed LLM training approaches like data parallelism, tensor parallelism, and pipeline parallelism. It also mentions commonly used libraries and frameworks for LLM training, indicating the technical complexity and collaborative efforts involved in developing these models.
- The article addresses the challenges associated with LLMs, such as the need for substantial computational resources, the potential for generating biased or harmful content, and the difficulties in ensuring model robustness and interpretability. It highlights ongoing research efforts to tackle these issues, including methods for efficient utilization, alignment with human preferences, and the development of safer and more reliable models.
- The article emphasizes the role of fine-tuning and adaptation stages in enhancing LLMs' performance on downstream tasks. It explores various fine-tuning approaches, including instruction-tuning with manually created datasets, alignment with human preferences, and the use of synthetic feedback, underscoring the importance of fine-tuning in achieving task-specific improvements.
- The article provides insights into future directions for LLM research, suggesting areas for improvement such as enhancing model interpretability, reducing environmental impact, and developing more nuanced approaches to model alignment. It calls for continued innovation and collaboration within the research community to advance the state of the art in LLMs.
- The article discusses the importance of fine-tuning in the context of LLMs, highlighting it as a crucial step for adapting pre-trained models to specific tasks and improving their alignment with human preferences and ethical standards.
- The article outlines various approaches to instruction-tuning, including the use of manually created datasets and datasets generated by LLMs themselves. Models such as T0, mT0, and Tk-Instruct are mentioned as examples that have undergone fine-tuning using these diverse datasets, demonstrating significant improvements in both task-specific performance and the ability to generalize to unseen tasks.
- The article emphasizes the role of fine-tuning in aligning LLMs with human preferences, a process crucial for mitigating issues such as biased, harmful, or inaccurate content generation. Approaches like InstructGPT, which utilize human feedback for fine-tuning, are discussed for their effectiveness in producing more helpful, honest, and ethical outputs from LLMs.
- The article also addresses the utilization of fine-tuning to increase the context window of LLMs, thus enhancing their ability to process and generate longer texts. Various techniques and models successful in expanding context lengths through fine-tuning are mentioned, underscoring the potential of fine-tuning in improving comprehension and response generation capabilities of LLMs.
- The article highlights research focused on making fine-tuning more sample-efficient, aiming to achieve high model performance with less data. This aspect of fine-tuning is crucial for reducing computational resources and making the fine-tuning process more sustainable and environmentally friendly.
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Abstract
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse topics such as architectural innovations, better training strategies, context length improvements, fine-tuning, multi-modal LLMs, robotics, datasets, benchmarking, efficiency, and more. With the rapid development of techniques and regular breakthroughs in LLM research, it has become considerably challenging to perceive the bigger picture of the advances in this direction. Considering the rapidly emerging plethora of literature on LLMs, it is imperative that the research community is able to benefit from a concise yet comprehensive overview of the recent developments in this field. This article provides an overview of the existing literature on a broad range of LLM-related concepts. Our self-contained comprehensive overview of LLMs discusses relevant background concepts along with covering the advanced topics at the frontier of research in LLMs. This review article is intended to not only provide a systematic survey but also a quick comprehensive reference for the researchers and practitioners to draw insights from extensive informative summaries of the existing works to advance the LLM research.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023 AComprehensiveOverviewofLargeLa | Humza Naveed Asad Ullah Khan Shi Qiu Muhammad Saqib Saeed Anwar Muhammad Usman Nick Barnes Ajmal Mian | A Comprehensive Overview of Large Language Models | 10.48550/arXiv.2307.06435 | 2023 |