Death of the Novel(ty): Beyond n-Gram Novelty as a Metric for Textual Creativity

Arkadiy Saakyan, Najoung Kim, Smaranda Muresan, Tuhin Chakrabarty · Sep 26, 2025 · Citations: 0

General Llm As Judge Pairwise Preference

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Moderate trust

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Best use

Secondary protocol comparison source

What to verify

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Evidence quality

Moderate

Derived from extracted protocol signals and abstract evidence.

Abstract

N-gram novelty is widely used to evaluate language models' ability to generate text outside of their training data. More recently, it has also been adopted as a metric for measuring textual creativity. However, theoretical work on creativity suggests that this approach may be inadequate, as it does not account for creativity's dual nature: novelty (how original the text is) and appropriateness (how sensical and pragmatic it is). We investigate the relationship between this notion of creativity and n-gram novelty through 8,618 expert writer annotations of novelty, pragmaticality, and sensicality via close reading of human- and AI-generated text. We find that while n-gram novelty is positively associated with expert writer-judged creativity, approximately 91% of top-quartile n-gram novel expressions are not judged as creative, cautioning against relying on n-gram novelty alone. Furthermore, unlike in human-written text, higher n-gram novelty in open-source LLMs correlates with lower pragmaticality. In an exploratory study with frontier closed-source models, we additionally confirm that they are less likely to produce creative expressions than humans. Using our dataset, we test whether zero-shot, few-shot, and finetuned models are able to identify expressions perceived as novel by experts (a positive aspect of writing) or non-pragmatic (a negative aspect). Overall, frontier LLMs exhibit performance much higher than random but leave room for improvement, especially struggling to identify non-pragmatic expressions. We further find that LLM-as-a-Judge novelty ratings align with expert writer preferences in an out-of-distribution dataset, more so than an n-gram based metric.

Low-signal caution for protocol decisions

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The abstract does not clearly name benchmarks or metrics.

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Should You Rely On This Paper?

This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.

Best use

Secondary protocol comparison source

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

The abstract does not clearly name benchmarks or metrics.

Trust level

Moderate

Usefulness score

57/100 • Medium

Useful as a secondary reference; validate protocol details against neighboring papers.

Human Feedback Signal

Detected

Evaluation Signal

Detected

Usefulness for eval research

Moderate-confidence candidate

Extraction confidence 65%

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What We Could Verify

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