Exploring Context Strategies in Language Models: The Significance of Context Size
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Table of Contents
As Language Models (LLM) like ChatGPT become more prominent, understanding the significance of context length is critical.
Developers must choose between small and large context strategies to optimize the performance and value of their applications.
This article explores the differences between small and large context approaches, their advantages and disadvantages, and their impact on application development.
What is the Context of an LLM?
Understanding ChatGPT Context Length
One of the critical components of Language Models (LLM) like ChatGPT is the context that the model uses to generate meaningful and relevant responses.
The model considers the conversation’s history of developing an appropriate output in a session chat context. However, the context length restricts the responses these models can produce.
Typically, the context length differs across various LLM generations. Recent research indicates context lengths of about 1 million tokens, but it’s crucial to remember that a token isn’t always equal to a word – it could be any symbol or character.
Exploring Context Strategies: Small vs. Long Context
There are two primary strategies to provide context to an LLM:
- Small context strategy: This only loads the context essential for the specific task.
- Long context strategy: This involves loading all available content into the model, thus providing a more comprehensive context.
The choice of context size can significantly impact the application or business using the LLM.
The Business Impact of Context Choices
Relying on an ever-growing context, which means using the long context strategy, is a safe bet. Instead of right-sizing the context, providing everything possible to the LLM is easier.
However, this approach may lead to dependence on the latest and largest LLMs, which could have unintended consequences.
Some use cases, like loading all available technical documentation into the context, might even exceed a massive context of 1 million tokens.
Research into more powerful and efficient AI models is ongoing, but it is unclear when they will be available, how far they will support the business case, or which hardware requirements they will have.
Managing Context
It is essential to manage the context in LLM-based architectures effectively. Depending on the task, you may need to load the right amount of data into the context.
It might be tempting to pack everything, but sometimes, a small and carefully managed context is more appropriate. Context can be controlled using different formats, such as vector databases, text files, or JSON files.
Having an idea or strategy for managing the context makes sense if you expect the context to grow soon. You need to expect that the content you will use to augment the context will also increase.
Generative AI models like Stable Diffusion do not have a context. AI image generators like Stable Diffusion allow fine-tuning, which needs significant compute resources and is not as flexible as loading content into a context on demand.
A context management strategy might be helpful here since you can create text prompts on demand or find related images using the embeddings.
In the long run, a context management strategy might pay off. It is easier to rely on a vast context like the 48k token context of GPT-4, but it might turn into a vendor-lock situation where you cannot replace GPT-n easily.
Big LLM Approach: Pros and Cons
The big LLM approach, which essentially involves using an LLM that covers the whole NLP stack, allows developers to concentrate on building application features. While this one-size-fits-all LLM could seem like a convenient solution, it’s likely to act as a jack of all trades, master of none.
Big LLMs often have high hardware requirements, leading to centralization. This kind of centralized approach can create bottlenecks in the architecture, primarily due to rate limitations.
While the application’s implementation may be more straightforward, its lack of modularity could limit its effectiveness.
Advantages of the Big LLM Approach
- Fast iteration during application development
- Simplified application architecture
- Streamlined operations and deployment
Disadvantages of the Big LLM Approach
- Enforced centralization
- Potential application bottlenecks due to rate limitations
- Suboptimal performance
- Limited cost control
- Inadequate suitability for every NLP task
Small LLM Approach: Pros and Cons
A small LLM, equipped with a smaller context, might be sufficient for specific tasks, albeit with some restrictions, such as support for only one language or lack of code support.
Developers can mitigate these limitations by using specialized AI models for specific tasks.
Small LLMs have lower hardware requirements than their larger counterparts, enabling a more modular application architecture. The small LLM approach can also offer better performance while handling specific tasks.
Advantages of the Small LLM Approach
- Flexibility for mitigating model limitations
- Modular architecture allowing effortless AI model replacement
- Decentralized approach
Disadvantages of the Small LLM Approach
- Increased application complexity due to multiple smaller LLMs
- More complex deployment and operations
Big LLM vs. Small LLM Approach
For developers, choosing between the big LLM and the small LLM approach often comes down to the project requirements and stage of development.
The big LLM approach might be suitable for rapid development and iteration, while the small LLM approach could be more suitable for specific use cases or during later-stage development.
Context Length Considerations
The primary motivation for using a big LLM is to increase context length, enabling a more straightforward application architecture.
However, shorter contexts might be more controllable and require optimization to achieve the desired outcome.
Conclusion
While relying on an ever-growing context may be beneficial in the short run, it could lead to vendor lock-in and reduced potential for optimizing specific NLP tasks.
Considering the context requirements carefully and choosing an approach that balances technical feasibility with business value is essential.
Developers should not solely depend on bigger and bigger LLMs; instead, they should focus on optimizing the context in a well-rounded manner.