Understanding the Limitations
Large Language Models like ChatGPT have text size limitations for both input and output. For instance, ChatGPT-3 has a maximum token limit, where a token could be as short as one character or as long as one word. This limit includes both the question you ask and the answer you receive. If your query is too long, you may need to shorten it, or you might receive a truncated answer. Knowing these constraints is the first step in effectively using an LLM.
Crafting Concise Inputs
If you have a lengthy question or a complex scenario that you’d like to discuss, try to condense it without losing the essential elements. Remove unnecessary details and focus on the core question. For example, instead of giving a long backstory for why you need to know the capital of a country, you could simply ask, “What is the capital of France?” The more concise the input, the more space is available for a detailed output.
Handling Long Outputs
If you expect a long answer, consider breaking your query into smaller, more manageable questions. This way, you can ensure that the output for each question stays within the text limit, and you can piece together the complete answer. For example, if you’re asking for a detailed explanation of a complex topic like climate change, you could break it down into smaller questions like “What causes climate change?”, “What are its effects?”, and so on.
Using Follow-up Questions
If the output is truncated or if you need more information, use follow-up questions to get additional details. This iterative approach allows you to dive deeper into a subject without hitting the text limit. Just remember to refer to the previous answer in your follow-up to make it clear that you’re seeking additional information on the same topic. For example, if you asked about the causes of climate change and got a truncated answer, you could follow up with, “Could you tell me more about how human activities contribute to climate change?”
Checking for Completeness
After receiving an answer, always check to see if it’s complete and addresses your query fully. If the answer seems cut-off or if crucial information is missing, that’s a sign you might have hit the text size limit. In such cases, consider rephrasing your question to make it more focused, or use follow-up questions to get the missing information.
In summary, working with text size limitations in LLMs involves understanding the constraints, crafting concise inputs, breaking down large queries, and using follow-up questions effectively. By being mindful of these aspects, you can ensure that you get the information you need without running into the limitations imposed by the model’s architecture.