Elias in the Lighthouse, Again? Diagnosing Low Diversity in LLM Stories

Technical Summary: Elias in the Lighthouse, Again? Diagnosing Low Diversity in LLM Stories

Problem Statement
Large Language Models (LLMs) exhibit a phenomenon known as "mode collapse," where generative outputs become increasingly homogeneous, particularly in creative writing tasks. While prior research has identified this trend, the specific mechanisms driving the convergence toward identical story structures remain poorly understood. This paper investigates the surprising lack of variability in stories generated by current frontier models when prompted with simple instructions like "write a story." The authors observe that models from OpenAI, Anthropic, Google, and the Allen Institute for AI (AI2) consistently produce narratives featuring the same specific character names, settings, and professions, despite these elements being rare in both published literature and pre-training data.

Methodology
To characterize this repetition, the authors conducted a large-scale sampling experiment:

• Data Generation: They generated 20,000 stories using four current models (Claude Haiku 4.5, Gemini 3.1 Flash-Lite, GPT-5.4-Mini, and OLMo 7b Thinking). Each model was prompted five times with variations of "write a story" (e.g., "Please write a story," "Tell me a story"), resulting in 1,000 stories per model.
• Vocabulary Extraction: Using GPT-5.4-nano, the authors extracted token spans corresponding to story settings, character first names, and professions. They filtered these candidates through a three-step process: tokenization, retaining the highest-frequency token per story/category, and keeping only tokens emitted by at least half of the models.
• Core Token Identification: A changepoint analysis was applied to identify a minimal "Core" vocabulary of 11 tokens that appeared most frequently across the corpus.
• Comparative Analysis: The frequency of these Core tokens was measured in Parts Per Million (PPM) and compared against:
  • Contemporary English literature (CONLIT corpus).
  • Amateur fiction (Reddit's r/writingprompts).
  • Pre-training data (OLMo 3's web data and fiction subsets).
  • Post-training data (OLMo 3's SFT, DPO, and RL datasets).
• Topic Modeling: An LDA topic model was trained on OLMo 3's post-training story corpus to determine if "lighthouse" stories formed a distinct genre or were distributed across various topics.

Key Results

• Extreme Homogeneity: 88.3% of the 20,000 generated stories contained at least one of the 11 Core tokens. Over half (51.2%) of all stories featured a "lighthouse," and 66.6% contained a specific combination of the location "lighthouse," the name "Elias," and the profession "keeper."
• The "Core" Vocabulary: The 11 tokens include names (Elias, Mara, Elara), professions (keeper, clockmaker, baker, fisherman, librarian, mayor, conductor), and the location "lighthouse."
• Discrepancy with Training Data:
  • Literature/Web: Core tokens are significantly more frequent in generated stories than in published fiction (e.g., "Elias" is 900 times more frequent in the generated corpus than in CONLIT) or general web data.
  • Pre-training: In OLMo 3's pre-training data, Core token rates were negligible.
  • Post-training: While OLMo 3's post-training data contained these tokens at higher rates than pre-training data, the absolute prevalence was still low. Only 3,053 out of 78,958 stories in the post-training set (3.8%) contained any Core token.
• Alignment Stage Impact: The density of Core tokens was found to be 5–8 times higher in alignment datasets (SFT, DPO, RL) compared to WildChat-derived stories, despite alignment data comprising only a fraction of the total post-training volume. Specifically, DPO and RL stages contributed a higher proportion of Core stories than the initial SFT stage.
• Topic Distribution: "Lighthouse" stories did not cluster into a single topic within the post-training data. Instead, they were scattered across diverse topics, including fan fiction, technical writing, and even inappropriate humor, suggesting the model learns to associate these specific tokens with "safe" or "preferred" outputs regardless of the surrounding context.

Significance and Claims
The paper claims to be the first study to trace a specific instance of mode collapse in creative writing back to its training inputs. The authors argue that the dominance of "Elias in the Lighthouse" narratives cannot be explained by the prevalence of these words in the raw training corpus. Instead, they posit that the phenomenon is driven by the interaction between small, specific datasets and powerful alignment algorithms (SFT, DPO, RL).

The authors suggest that during alignment, models may be trained to avoid references to copyrighted characters and adult content, inadvertently funneling the model toward a narrow set of "safe," generic, pre-modern tropes (e.g., lighthouses, clockmakers) found in preference data. This results in a disproportionate influence from a tiny fraction of the training data (less than 4% of post-training stories) on the model's final output behavior. The study highlights that current models do not simply mimic dominant patterns in their corpora but rather converge on specific, safe modes of generation that may not reflect the diversity of human storytelling.