The Challenges of Text Length Limitation with GPT: Issues Encountered with Large Language Models

In this post we discusses the challenges associated with limiting text length when using Large Language Models (LLMs). These models, such as GPT, possess remarkable capabilities for generating text but can face difficulties in predicting and controlling the length of their outputs. Let’s delve deeper into this topic.

Overcoming Challenges in Text Length Limitation

LLMs, including GPT, are designed to mimic human writing style and logic. However, this characteristic poses challenges when it comes to maintaining control over the length of generated texts. Here are some key issues:

Contextual Information Processing: LLMs analyze the context of the input text to generate relevant and coherent outputs. However, this process can lead to the addition of extra information for context explanation, which may exceed the desired text length. For example, if we provide a prompt like “Describe the history and significance of the Eiffel Tower in 100 words,” an LLM might provide a detailed historical account that goes beyond the specified word limit, making the response lengthier than desired.

Creative Expression: LLMs exhibit a creative element, enabling them to produce unique and original texts. Occasionally, this creativity leads to the development of ideas or the inclusion of additional information beyond what is strictly necessary within a limited text length. When asked to write a concise product description in 50 words, an LLM might generate a captivating narrative with vivid details that surpasses the length constraint, providing a more engaging but lengthier description.

Lack of Predictability in Output Length: LLMs are not specifically designed to accurately predict the length of their outputs. While certain measures can be taken to reduce the likelihood of longer texts, achieving precise control over length remains challenging. When setting a maximum length constraint of 200 words for a document might lead to outputs ranging from 150 to 200 words, depending on the complexity of the topic and the LLM’s interpretation. This is not a desired result if you want to meet certain output lengths with upper or lower limits. Sometimes both is true when it comes to Google-Ads. You have an upper limit given by Google, but also a lower limit which you want to achieve to gain more real-estate when your ad shows up on the results and to improve your quality score.

Why the LLM cant limit its own output length

Models such as GPT are great at generating answers that sound smart. However, they are not capable of predicting their own output. This means, that when the Model starts generating its response it does not know where this response will end. This is a problem, as the end of the output has to be taken into account, if you want to explain all important points before this end occurs. But because these LLMs generate one letter or one word at a they dont know what comes next. Even the “.” at the end of a sentence is just what they think should come next. This is an inherent logic of LLMs and even machine-learning. To overcome this obstacle there are a few steps we can take, to limit the output length.

Strategies for Influencing Text Length

Despite the challenges associated with controlling text length in LLMs, several approaches can be employed to exert influence:

Context-Specific Input: Providing LLMs with precise and specific inputs that define the context and desired information can minimize the probability of extraneous text. For example, if we want a concise summary of a book, we can provide the LLM with a specific question like “Summarize the main plot, characters, and themes of the book ‘To Kill a Mockingbird’ in 100 words.” This clear input guides the LLM to focus on the essential aspects within the given word limit.

Temperature Control: The “temperature” parameter of an LLM affects the randomness and diversity of its outputs. Lowering the temperature (e.g., 0.5) produces more conservative and predictable texts, while raising it (e.g., 1.0) leads to greater variety and creativity. For instance, when generating responses to a writing prompt, setting a lower temperature can make the LLM more cautious and less likely to produce lengthy or tangential texts, ensuring adherence to the desired length.

Input Constraints: One can attempt to constrain the input provided to the LLM by implementing specific limitations. For instance, using placeholders for certain sections of the text ensures that the LLM stays within those guidelines, reducing the likelihood of verbose or tangential texts. For example, if we want the LLM to generate a short business proposal, we can structure the input with fixed sections such as “Introduction”, “Product Description” and “Conclusion” ensuring that the LLM focuses on providing concise information within each section.

Post-Processing and Summarization: After generating the text using the LLM, post-processing techniques can be employed to shorten it to the desired length. This may involve manual revision or the use of summarization algorithms to extract key points and essential information, ensuring a more concise text. For instance, if the LLM generates a lengthy research paper, an editor can employ summarization techniques to extract the main findings and conclusions, condensing the content to fit the desired length while preserving the essential information.

In conclusion, addressing the challenges of text length limitation in Large Language Models requires strategic approaches. By providing context-specific inputs, utilizing temperature control, implementing input constraints, and leveraging post-processing techniques, we can exert influence over text length. These considerations allow us to obtain precise and high-quality outputs from Large Language Models while maintaining control over text length.

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One response to “The Challenges of Text Length Limitation with GPT: Issues Encountered with Large Language Models”

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