Four LLM Trends Since ChatGPT: Exploring Their AI Builder Implications
Introduction:
In October 2022, an article on LLM selection for specific NLP use cases was published. Since then, there have been significant advancements in AI, and this article will explore the trends and their implications for AI builders. The article will cover topics such as task selection for autoregressive models, the evolving trade-offs between commercial and open-source LLMs, LLM integration, and the mitigation of failures in production. The popularity of generative AI has pushed autoregressive models to the forefront, while autoencoding models await their moment. Open-source models are competing with commercial offerings, driving innovation in LLM efficiency and scaling. The article also discusses how LLMs are becoming operational with plugins, agents, and frameworks.
Full Article: Four LLM Trends Since ChatGPT: Exploring Their AI Builder Implications
October 2022 saw significant advancements in the field of LLM selection for specific NLP use cases. In this article, we will explore the trends of the past months and discuss their implications for AI builders. Some of the topics we will cover include task selection for autoregressive models, the evolving trade-offs between commercial and open-source LLMs, as well as LLM integration and the mitigation of failures in production.
1. Generative AI and Autoregressive Models:
The popularity of ChatGPT has raised questions about its potential to replace other AI models. However, it’s important to note that ChatGPT focuses on generative AI tasks, not analytical AI. Autoregressive models, like the GPT family, are ideal for tasks such as conversation, question answering, and content generation. These models excel at generating the next token or sentence. On the other hand, autoencoding models, which are better suited for analytical tasks like information extraction and distillation, have taken a backseat. However, for B2B use cases that require concise insights, autoencoding models can still be relevant.
2. Open-Source vs. Commercial AI:
There has been ongoing debate about the relationship between open-source and commercial AI. In the short term, commercial AI often outperforms open-source due to its access to larger amounts of data and resources. However, the open-source community has focused on increasing the efficiency of LLMs by doing more with less. This not only makes LLMs more affordable and accessible but also more environmentally sustainable. By reducing compute and memory usage, the open-source community has made LLMs more efficient. Additionally, narrowing down training data and using instruction fine-tuning have proven effective in achieving optimal performance with fewer resources.
3. Balancing Efficiency and Output Quality:
While open-source models focus on efficiency, commercial offerings prioritize output quality. Commercial LLMs have the advantage of larger model and data sizes, resulting in higher-quality outputs. However, concerns around governance and regulation are arising, as companies may develop LLMs that align solely with their commercial objectives. As resources are allocated to increase efficiency, the learning curve of LLMs begins to flatten. This provides relief amidst fears of AI surpassing human capabilities. However, the emergence of new, unexpected capabilities in LLMs remains unpredictable and far from providing robust commercial value.
4. Open-Source LLM Advancements:
Efforts to increase the efficiency of open-source LLM finetuning and inference have loosened the resource bottleneck. Many companies are now considering deploying their own LLMs to overcome the usage cost and quota limitations of commercial LLMs. However, development and maintenance costs, as well as technical skills, are still required. Choosing between open-source and commercial LLMs is a strategic decision that involves various trade-offs, including costs, availability, flexibility, and performance. Starting with commercial LLMs to validate the business value and transitioning to open-source models later is a common approach. However, this transition can be challenging, as open-source models may not meet specific requirements.
The field of LLMs is continually evolving, and AI builders need to stay updated on the latest trends and considerations. By understanding the capabilities and limitations of different types of LLMs, developers can harness their potential for various NLP use cases.
Summary: Four LLM Trends Since ChatGPT: Exploring Their AI Builder Implications
In October 2022, an article was published discussing the trends and implications of AI in the field of LLM (Language Model Machine) selection for specific NLP (Natural Language Processing) use cases. The article covers topics such as the dominance of generative AI models like ChatGPT, the potential for a resurgence of autoencoding models for analytical tasks, and the competition between open-source and commercial AI models. It also explores the efforts to increase LLM efficiency and scaling through techniques such as FlashAttention, parameter-efficient fine-tuning, and instruction fine-tuning. Additionally, the article highlights the growing operational capabilities of LLMs with the use of plugins, agents, and frameworks.
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