Exploring the Token Count Challenge in AI Story Generation

Introduction

AI has empowered us with the ability to craft unique, dynamic narratives, a marvel we’re happily exploring with Infinity Stories. But like every pioneering journey, we sometimes encounter obstacles on the path. One such challenge we recently faced is related to token count, the units the AI uses to break down and understand text.

In this post, we’ll delve into a peculiar quirk of AI storytelling: the lack of a minimum token count option. We’ll explain the challenge in detail and discuss some potential solutions we’ve been exploring.

Understanding Token Count in AI Storytelling

To fully appreciate the problem, it’s essential to understand what tokens are and their role in AI storytelling. In the world of Natural Language Processing (NLP), tokens are the units the AI uses to break down and understand text. These can be as short as one character or as long as one word. For instance, in the sentence “Infinity Stories is awesome”, there are four tokens: “Infinity”, “Stories”, “is”, “awesome”.

When crafting stories with AI, we can specify a maximum token count so that the AI doesn’t exceed a certain length. However, the AI doesn’t have a minimum token count setting, creating a unique challenge when we want to ensure the story reaches a specific length.

The Challenge

Imagine we’re generating a new story. We want each page to be a minimum of 1,000 tokens but no more than 2,000 tokens. While we can set the maximum token count to 2,000, we cannot specify a minimum. As a result, the story generated could range anywhere from a handful of tokens to the 2,000 token max. The absence of a minimum token count setting means the story generated could fall short of our desired length.

Potential Solution: Post-Processing Checks

In the realm of AI language models, sometimes the answers are not found within the settings and parameters we can manipulate but are instead crafted out of the creative use of the resources at our disposal. This is where post-processing checks come into play.

Post-processing checks are a unique approach that entails checking the length of the generated text after our AI language model has completed its task. If the generated text falls short of our desired length, we generate additional content. This is done by prompting the AI to continue the narrative from where it ended, resulting in a seamless and engaging extension of the story.

Here’s a Python snippet that shows this approach:

def generate_story(parameter1, parameter2, word_count=2000, min_word_count=1600):
    prompt = #prompt goes in here

    # Continually generate story until the minimum word count is met
    generated_text = ""
    while len(tokenizer.tokenize(generated_text)) < min_word_count:
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": "Role of ChatGPT Here."},
                {"role": "user", "content": prompt}
            ],
            max_tokens=word_count,
            temperature=0.8,
        )

        generated_text += response['choices'][0]['message']['content'].strip()
        prompt = "Continue the story from where you left off."

    return generated_text

This Python code snippet incorporates post-processing checks into our story generation. A while loop ensures the story generation continues until we get as close to our max word count of 2000, maintaining a coherent and engaging narrative by seamlessly prompting the AI to continue the story. If you look at the code however, we don’t set the min word count to 2000 as well but rather 1600 in order to account for any errors. For instance, it leaves room for the AI to properly end a sentence or a thought, even if it means producing a few less tokens. We don’t want the AI to abruptly cut off the narrative just because it hit the maximum word count.

It’s important to note that this process involves intricate maneuvering. To ensure a coherent and smooth continuation, careful attention must be given to the prompt used for generating the additional content. This solution’s effectiveness can also be influenced by the specific details and context of the story, making it a challenge worth tackling.

While implementing post-processing checks presents its unique hurdles, it offers a promising path towards addressing the issue of short story outputs. It is not a foolproof solution, but it opens up avenues for further experimentation and optimization of our AI story generation process.

As we continue to refine this approach and explore others, we remain committed to pushing the boundaries of AI storytelling, ensuring that every story told is as engaging, entertaining, and perfectly paced as you would expect from Infinity Stories.

Conclusion

As we navigate the fascinating realm of AI-powered storytelling with Infinity Stories, challenges like these provide intriguing problems to solve. In addressing the token count challenge, we’re refining our process, learning more about the inner workings of AI, and improving the quality of the stories we generate.

The journey, like our stories, is unfolding one step at a time, leading us to exciting new horizons in AI-powered storytelling.

Keywords: AI, AI Storytelling, Token Count, OpenAI, Infinity Stories, NLP, Language Model