What is Good? Extracting and Testing Implicit Theories of Literary Quality from LLM Reasoning Traces
This two-study investigation explores how reasoning-enabled LLMs evaluate literary quality. Study 1 constructs a benchmark of 30 texts across six quality tiers, extracting an implicit theory that values intentionality, craft, depth, and distinctive voice over correctness. Study 2 probes this theory through systematic degradation of canonical prose, finding that vocabulary simplification causes the smallest quality loss while structure and voice loss are far larger. The results suggest LLM judgments are holistic, author-specific, and more sensitive to structural than lexical features, with implications for automated writing feedback and computational aesthetics.
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[Submitted on 7 Apr 2026]
Title:What is Good? Extracting and Testing Implicit Theories of Literary Quality from LLM Reasoning Traces
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Abstract:What makes writing "good" remains a persistent question in literary studies and computational linguistics. We present a two-study investigation of how reasoning-enabled LLMs evaluate literary quality.
In Study 1, we construct a benchmark of 30 real texts spanning six quality tiers, from canonical literature to anonymous forum posts, and extract the model's implicit theory of quality from its reasoning traces. Across five DeepSeek replications, the model achieves 79.3% mean tier-classification accuracy. The traces reveal a consistent stated theory: the model values intentionality over correctness, prioritizing craft, depth, and distinctive voice. A familiarity experiment with style-matched but unrecognizable passages suggests that source recognition may inflate scores, although this is confounded by genuine quality differences between canonical originals and researcher-written pastiches.
In Study 2, we probe this theory through systematic degradation of five canonical prose passages. We apply six manipulations - vocabulary simplification, rhythm flattening, imagery removal, voice genericization, structure simplification, and combined degradation - and reevaluate each version. Vocabulary simplification causes the smallest quality loss (0.41 +/- 0.46 points), far below structure (2.78) or voice (2.34) loss. Combined degradation is devastating (-5.64) but subadditive. An exploratory comparison with Qwen QwQ shows the same broad qualitative pattern.
Together, these studies suggest that LLM judgments of writing quality are holistic, author-specific, and more sensitive to structural than lexical features, with implications for automated writing feedback and computational aesthetics.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2607.20425 [cs.CL]
(or arXiv:2607.20425v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.20425
arXiv-issued DOI via DataCite
Submission history
From: Birger Moell [view email] [v1] Tue, 7 Apr 2026 13:29:12 UTC (216 KB)
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