GPT Models Encounter Length Issue in Context.

Context Size Vs. Model Size: Solving the Context Length Problem in AI Chatbots

In recent AI research, the debate on model size has given way to the importance of context size. While smaller LLMs trained on more data are currently the best option, context length has become a critical focus for improving AI tools like ChatGPT for enterprise use. This article will explore the importance of context length and the challenges it poses for AI chatbots.

Why is Context Length Important?
Since the transformer architecture became more popular, a small section of research has been working around increasing the sequence length to improve model response accuracy. For AI chatbots, having a clear and complete context means generating more relevant and meaningful responses, while only loading essential conversation parts can produce short context strategy.

GPT’s Context Length Limitation
Despite their magical capabilities, OpenAI’s models like ChatGPT have a context length limit of 4,096 tokens, which was increased to 32,768 tokens only for a limited-release full-fat version of GPT-4. Crossing this limit means the model would hallucinate or cease working. For instance, ChatGPT processing a 2,000-word spell-check session could only work on 800-900 words before hallucinating and offering unrelated questions.

Solving the Context Length Problem
Research teams from rival companies have been working on addressing the context length problem. For example, Anthropic AI’s chatbot Claude can process up to 75,000 words or 100,000 tokens, enough to read and process The Great Gatsby novel. Salesforce has also released a family of open-source LLMs called CodeT5+, offering richer context processing, while using a more flexible encoder-decoder architecture.

Another breakthrough was Meta AI’s research team’s multimodal transformer architecture proposal, “MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers,” that addresses the context length problem by segmenting sequences into separate patches with a local submodel within these patches and a global model between them. The resulting architecture is more scalable, reducing cost while allowing more expressive models using big feedforward layers per patch instead of per position.

Conclusion
For chatbots seeking human-level intelligence, context is everything; it provides a complete fit-for-purpose communication experience. However, the expensive tokenization cost of transformers raises the big question of whether the investment is worthwhile. For example, GPT-4’s 32k context length costs $1.96, which is prohibitive considering these tools’ wide general purpose usage across organizations.

Editor Notes
As AI development progresses, Solving the context length problem is paramount, and this article offers critical insights into the development of AI chatbots, a critical component of overall AI technology. For more AI-related news and analysis, visit GPT News Room.

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