My colleague Jessica Witte has just shared a preprint where she compared the emotional arcs of the stories we generated using gpt-4o-mini to those of human-authored (pre-2022) stories from the subreddit r/WritingPrompts, conveniently gathered in this dataset. She found a distinct difference in the endings of the LLM-generated stories: both human and LLM stories tend to have “happy endings” as measured by sentiment analysis, but while human stories have quite a sharp upturn in the last 10% of the story, the LLM’s stories start getting “happier” much earlier, from around the 70% mark, and the upwards curve takes longer. Also, as many other studies have found, the sentiment arc sof human stories are far more different from each other than thos of the LLM-generated stories. I’ll explain the gist of her argument in this blog post, but I do also want to draw your attention to her literature review, which is the best I’ve read for getting an overview of findings from computational analyses of AI-generated stories. Honestly, it’s a really well-written paper: “The Shapes of AI-Generated and Human-Authored Stories“.

This matches our sense that LLM-generated stories have long, drawn-out endings, which of course makes sense since they’re written additively. The LLM generates more words based on the words that are already there, and then more based on those words and so on and so on. So you get ridiculous things where the story seems to end, then it ends again, then there’s an “End” in bold text, and then after that there’s yet another ending. (See DK_3 in our dataset for an example).

An emotional arc plots positive vs negative sentiment in a text over time, and scholars like Kate Elkins and Matthew Jockers have argued that this is a proxy for the dramatic arc of a plot.

Here is Jessica’s plot of sentiment arcs in the human stories:

See what a mess human stories make? So what Jessica did is split each story into ten bins and run a sentiment analysis on each of these tenths of the story. If the sentiment is very positive it scores 1, if it’s very negative it scores -1. So you see some stories start sad (or using a lot of sad words, anyway) and others happy, and the sentiment goes up and down in a tangled mess.

OK, here’s 200 of the stories in the GPT stories dataset. See how much more regular this is? And they are even more regular towards the end. The AI-generated stories sentiment is pretty diverse at the start, but towards the end they all get more positive.

Here’s an example of one of these neverending endings. Honestly, it could have ended after the first of these paragraphs, which is about 70% of the way into the text. As you can see, each paragraph is a new ending. This is from GR_5, one of the “Greek” stories in our dataset. Illustration purposes only: skim this, you don’t have to read the whole quote!

Callista approached him, her ethereal form shimmering in the twilight. “The path of the heart is never easy, dear Lycos. You must learn to find balance between the worlds you inhabit. The beauty of nature exists both in the wild and in simplicity. You cannot seek to escape your life; you must embrace it.”

Chapter 7: The Harmony of Two Worlds

With her words echoing in his mind, Lycos reflected on his journey. He realized that he could embrace both worlds—the enchantment of the nymphs and the beauty of his simple life as a shepherd. He could find magic in the mundane and joy in the quiet moments.

From that day forward, he began to weave the lessons he had learned in the glade into his daily life. He shared stories of the forest’s wonders with the villagers, rekindling connections he thought lost. He taught them to appreciate the beauty of the world around them, to listen to the whispers of the trees, and to find joy in the rhythm of nature.

As time passed, the villagers began to trust Lycos once more. They learned to love the hills and the simple pleasures he had once celebrated. And though he could not return to the realm of the nymphs, he found solace in the knowledge that magic still existed within him.

On nights when the moon shone brightly, Lycos would sit by the spring, feeling the ethereal presence of Callista and the nymphs. He would close his eyes, allowing their laughter to fill his heart, knowing that he carried a piece of their magic with him always.

Epilogue: A Life Enriched

Years went by, and Lycos became a wise elder in his village, known for his stories and his deep connection to the land. The hills of Arcadia flourished under his care, and he taught the next generation the importance of balance—of finding joy in both the magical and the mundane.

And though the nymphs remained a distant memory, the whispers of the grove lingered in his heart, reminding him that every choice he made was a thread in the tapestry of his existence—a beautiful, intricate story that would continue to unfold with each passing day.

(I hope you just skimmed that. It’s long, isn’t it!)

OK, so Jessica went on to do something called ensemble analysis where she used four different sentiment models on all 200 human and 200 AI stories, and averaged each model’s results. Here’s the averaged out emotion arc for the human stories:

Notice how on average, there’s a really steep rise in sentiment just in the last tenth of the story.

Now look at the AI-generated stories. There’s a rise there too, but it starts much earlier, and is much more gradual.

Now, as Jesscia points out, there are still lots of caveats here and these are early results. Jessica is planning to run this across lots of other AI-generated stories, and compare to other sets of human stories too. But there definitely seems to be something here.

When I read “The Serpent in the Grove“, the short story that was the regional winner for the Caribbean of the Commonwealth Prize this May and that was found to be AI-generated, the long ending was one of things I noticed. I drew this plot diagram based on my close reading of the story. This is almost the opposite of a sentiment arc, because I was mapping rising and falling tension, so the highest point in my arc is the turning point of the story where Sita falls into the well and almost dies. Notice the very, very long epilogue in this story. And the line doesn’t even cover the 18 lines of short aphoristic morals that come after the main story has ended. AI-generated stories really take a long time to end.

I made the diagram by making an excel sheet with a row for each paragraph in the story where I wrote a very short summary of what happened and classified each according to its plot function, like this:

It’s a strange story, in terms of its plot, quite apart from all the overwrought metaphors that others have written about.

Anyway, have a read of Jessica’s preprint. And let me or obviously Jessica know if you have any feedback or ideas for what we should look at next!