Ask AI for a Story and You Often Get a Lighthouse Keeper Named Elias, a 20,000-Story Study Finds

Simply tell a large language model (LLM) to "write a story," and models built by different companies produce strikingly similar tales. The protagonist is named Elias, works as a lighthouse keeper, and lives in a lighthouse on a remote island battered by waves. A study by Sil Hamilton and Professor David Mimno of Cornell University in the United States quantifies this bias in AI storytelling using 20,000 generated texts. The paper, published on May 26, 2026 and titled "Elias in the Lighthouse, Again? Diagnosing Low Diversity in LLM Stories," is scheduled to be presented at EMNLP 2026, an international natural language processing conference to be held in Budapest, Hungary, from October 24 to 29.

The research team analyzed 20,000 stories generated by LLMs from four companies. At least one of 11 specific words appeared in 88.3% of all stories. "Lighthouse" appeared in 51.2% of stories and "keeper" in 48.1%. Among character names, "Elias" stood out at 26.5%, followed by "Mara" at 16.7% and "Elara" at 13.1%. These are descriptive statistics reported in the paper, without confidence intervals or similar measures, but they show that the outputs of different models tend to concentrate on a limited vocabulary.

Three words stand out in particular: "lighthouse," "Elias," and "keeper." In some combination, these words appeared in 66.6% of all stories. Furthermore, 56% of the stories generated by Anthropic's Claude carried the same title: "The Lighthouse Keeper's Secret." Settings were explicitly identified in 19,864 of the 20,000 stories, character names in 19,864, and occupations in 15,807, showing that each model repeatedly chooses similar places, characters, and professions.

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  "title": "Share of key elements across 20,000 AI-generated stories",
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  "series": [
    { "name": "Share of stories", "values": [88.3, 66.6, 51.2, 48.1, 26.5, 56.0] }
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  "caption": "Cornell University analysis of 20,000 generated stories. Only the last figure (56%) uses Claude-generated stories as its base.",
  "source": "arXiv:2605.26492"
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The results go beyond the impression that AI writes texts with a similar feel. Even though the models were asked for free-form creativity, the same setting device, a lighthouse, appeared in more than half of the stories. Why do models trained on vast amounts of data and developed by different companies choose the same small set of settings and names? The research team explored the cause by comparing the outputs with human-written novels and publicly available training data.

Analyzing 20,000 stories reveals a shared vocabulary bias across AI models

This study is not a psychology experiment in which human readers evaluate stories. It is a corpus analysis that statistically examines text generated by LLMs. Rather than subjectively assessing artistic quality or entertainment value, it analyzes the frequency of words used for character names, occupations, and settings.

The subjects were Anthropic's "Claude Haiku 4.5," Google's "Gemini 3.1 Flash-Lite," OpenAI's "GPT-5.4-Mini," and "OLMo 7B Thinking," an open-weight model from the nonprofit research institute Allen Institute for AI (AI2). All were available as of April 2026 and were accessed through OpenRouter. To secure enough samples on a limited budget, the team used relatively small models rather than each company's top-tier model, and data collection cost a total of $180. The authors state that larger and smaller models in the same family generally show similar story-generation tendencies, but the paper does not present results from systematically comparing larger models under the same conditions.

The prompts were deliberately vague and limited to five variations: "Write a story," "Please write a story," "Write me a story," "Tell me a story," and "Please tell a story." Each is a simple request to write a story. Each prompt was entered 1,000 times per model, generating 20,000 stories in total (4 models × 5 prompts × 1,000 runs). The text totals about 12.8 million words. A footnote in the paper says similar tendencies were seen with more complex prompts, but what this analysis systematically tested was these five short instructions.

To extract distinctive words from the generated stories, the team used "GPT-5.4-nano." It identified expressions corresponding to each story's setting, the main character's first name, and occupation. After checking the extraction results, the team narrowed the candidates based on frequency and commonality across models. This left 663 words in total: 247 for places, 71 for names, and 345 for occupations. Then, using the change-point detection method proposed by Killick et al. in 2012, the team selected 11 especially frequent words as "core words," based on the boundary where the frequency distribution changes sharply.

The core words include personal names such as "Elias," "Mara," and "Elara"; occupations such as "keeper," "clockmaker," "baker," and "librarian"; and the place word "lighthouse." Of the 61 words made up of these 11 plus the next 50 most frequent, at least one appeared in 98% of all stories. Stories containing all three elements of name, occupation, and place accounted for 49.1%. However, these figures depend on the team's word-extraction method and classification criteria, and they are not a measure that captures every aspect of story diversity. Even when words are shared, plot developments and content are not necessarily identical.

Compared with human novels, "Elias" appears about 900 times more often

Is AI's repeated choice of particular names and settings simply because human-written novels show the same tendency? To investigate, the team compared the 20,000 generated stories with a large corpus of human-written English fiction.

The comparison used the "CONLIT" corpus built by Piper in 2022. It contains 2,700 contemporary English-language novels published between 2007 and 2021, spanning 12 genres and about 287 million words. The team compared how many times a given word appears per million words, expressed as PPM (Parts Per Million), in the human literary works and the AI-generated works.

The result was striking. "Elias" appeared at 2,428 PPM in the AI-generated stories, versus just 2.7 PPM in CONLIT. In other words, "Elias" was used about 900 times more often in AI-generated works than in human-written contemporary novels. This is a comparison normalized by text volume, not a claim that the share of works featuring "Elias" is 900 times higher.

The team also examined not only commercially published novels but also amateur works on the internet. For this comparison they used posts from the Reddit creative-writing community r/WritingPrompts, collected by Huang et al. in 2024. In this data too, the frequency of "Elias" was similar to CONLIT, and no bias like that of AI-generated stories was found. Therefore, at least in the human creative works examined, the same concentration of words was not observed.

In AI-generated works, not only "lighthouse" but also the related word "light" was used frequently. It appeared in 16,784 of the 20,000 stories, averaging 3.2 occurrences per story. These results show that expressions related to lighthouses and light, not just particular proper nouns, permeate the generated works broadly. However, the comparison with human novels shows differences in observed word frequency and does not identify the cause of those differences.

Only 3.8% in training data, so why so frequent in AI output?

So is the training data the reason AI repeatedly features lighthouse keeper Elias? A obstacle to testing this is that the training data for commercial LLMs is not public. Outside researchers cannot closely examine the pretraining data or the preference data used in post-training for GPT-5.4-Mini, Claude Haiku 4.5, and Gemini 3.1 Flash-Lite. The team therefore analyzed AI2's "OLMo 3," whose training data is public, to see what kinds of data contain the distinctive words.

They focused in particular on "post-training," which takes place after the pretraining that builds basic language ability. In post-training, models are tuned to produce desirable responses through supervised fine-tuning (SFT), direct preference optimization (DPO), reinforcement learning (RL), and other methods. From OLMo 3's public post-training data, the team extracted 78,958 story-format texts and checked whether they contained the 11 words described above.

The result: only 3,053 stories, or 3.8% of the total, contained at least one core word. OLMo 3's pretraining used about 3.89 billion documents; taking the total number of training documents as about 4 billion, 3,053 stories corresponds to roughly \(7.71 \times 10^{-7}\). Of course, this compares the number of stories used in post-training with a vast number of documents including pretraining, and does not directly represent how much influence each piece of data had on the model. Even so, it confirms a large gap: words found in only a tiny number of works in the training data appear at high frequency at generation time.

Looking closely at the sources of the post-training data, the largest share of stories came from "WildChat," released in 2024 by Cornell University researchers. WildChat is a dataset of about one million real conversations between users and ChatGPT, and it has also been used to build other training datasets. According to the paper, OLMo 3's post-training data included 59,266 stories derived from WildChat, of which only 1,803 contained core words.

Comparing by post-training method, however, the share of works containing core words differed. It was 3.0% in supervised fine-tuning (SFT) data, versus 8.0% for direct preference optimization (DPO) and 10.9% for reinforcement learning (RL), relatively higher. This means that stories containing particular words are relatively common in the data from which models learn preferred outputs. However, frequency in data is not the same as influence on learning, and it cannot be concluded that DPO or reinforcement learning actually caused this bias.

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  "caption": "Comparison of OLMo 3's public post-training data (78,958 stories) and WildChat-derived stories",
  "source": "arXiv:2605.26492"
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To understand what the post-training stories consist of, the team also ran a topic analysis using latent Dirichlet allocation (LDA). When the stories were sorted into 10 topics, the main topics included many fan fiction works based on Japanese anime and games and American animated series. Some of these featured copyrighted characters, and some contained adult content or inappropriate humor.

Lighthouse stories, on the other hand, did not form a large independent topic and were scattered across multiple groups containing general fiction. In other words, the training data as a whole was not skewed toward lighthouse stories. Even so, when models wrote stories freely, lighthouses and particular character names were chosen repeatedly. As a hypothesis to explain this, the team points to the influence of "alignment," the tuning of AI safety and response quality.

One possibility is that in the process of tuning models to avoid copyrighted characters and adult content, safe and unproblematic stories became relatively more likely to be chosen. Lighthouse keeper stories may be one kind of "safe story" that fits these conditions, influencing generation choices more than their frequency in training data would suggest. However, this is a hypothesis presented by the research team, not a demonstration that alignment is the direct cause.

In an article in the university's news outlet Cornell Chronicle dated October 19, Hamilton mentioned that WildChat, released in 2024, may be one source of the phenomenon. Considering the timeline, he said he is fairly confident it was one of the early sources. But he and the research team say they still cannot give a definitive explanation for why models favor lighthouse stories. "Mode collapse," in which the diversity of generated content is lost as different models use shared datasets or data derived from them, may be involved, but that causal relationship remains a question for future verification.

What shapes AI creativity is still not understood

What the study confirmed is this: when four companies' relatively small LLMs, available as of April 2026, were given very short English prompts, the names, occupations, and settings of the stories they generated concentrated on a limited vocabulary. It is significant that the study quantitatively showed both that a common tendency appeared across models developed by different companies and that the same words appear far less often in human-written novels and publicly available training data. However, it cannot be generalized from these results that all LLMs write the same stories.

The mechanism that produces the vocabulary bias is also unexplained. Post-training and alignment may make particular expressions more likely to be chosen, but the study did not run ablation experiments that remove tuning steps one at a time, or experiments that retrain models after excluding specific datasets. The observation that core words made up a higher share of DPO and reinforcement learning data is not direct evidence that those methods amplified the bias. At this stage, the understanding is limited to the fact that a bias not explained by simple frequency in the training data arises in the generated output.

The scope of verification is also limited. The only model whose training data could be examined in detail was the open model OLMo 3, and it is not known how the same phenomenon arose inside GPT, Claude, and Gemini. In addition, the prompts systematically tested were limited to five short English ones, so it is unclear whether the same degree of bias would arise when detailed worlds or characters are specified, or when stories are requested in languages other than English, including Japanese. Going forward, in addition to comparison experiments that vary these conditions, it will be necessary to causally verify how the composition of post-training data and alignment methods affect the diversity of generated content.

Hamilton told the Cornell Chronicle that when language models are asked to write stories, similar works are generated repeatedly, and the repetition becomes clear when they are collected in large numbers. Even if each text looks natural and original on its own, comparing large volumes of output suggests that a limited set of expressions and settings may be used repeatedly. This does not immediately negate the literary value of individual AI-generated works, but it is a problem that cannot be overlooked in evaluating the diversity of creative writing by generative AI.

As AI-generated text circulates widely on websites and in publications, bias toward particular stories and expressions bears on the future diversity of content. This study showed that even when AI generates text from vast amounts of training data, its output is not necessarily diverse. Clarifying why different models converge on similar expressions could lead to the development of AI that enables more diverse creative work.