Ryan Heuser
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heuser.bsky.social
Ryan Heuser
@heuser.bsky.social
1.9K followers 2K following 240 posts
Assistant Professor of Digital Humanities @camdighum.bsky.social. Florida man abroad, lapsed Catholic, vulgar marxist; phd'd Stanford English, alum of Literary Lab. I make data about culture and write about forms of abstraction in (C18) literary history.
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heuser.bsky.social
Is there a list of collaborationist unis out there? So many now it's hard to keep track
November 29, 2025 at 9:09 PM
heuser.bsky.social
ah! I see what you mean
November 28, 2025 at 6:40 PM
heuser.bsky.social
This is already published in Intl Journal of DH! doi is 10.1007/s42803-023-00075-w
November 28, 2025 at 6:21 PM
heuser.bsky.social
Very useful, conceptually as well as technically: I didn't know about the "output_attentions" flag, I want to experiment with that soon.
November 28, 2025 at 12:29 PM
heuser.bsky.social
I like my contradictions sharpened
November 20, 2025 at 10:03 PM
heuser.bsky.social
How'd you know I'm hard at work on BernAI Bro
November 20, 2025 at 8:23 PM
heuser.bsky.social
Most of all, I recommend you check out all the incredible articles in this special issue (culturalanalytics.org/issue/12788), with work by Tess McNulty, Laura Chapot, @jeddobson.bsky.social, Alexandre Miller et al, and @katelelkins.bsky.social.
Vol. 10, Issue 3, 2025 | Published by Journal of Cultural Analytics
In this special issue, we bring together scholars from across multiple disciplines to reconsider the intersections of computation and form at this emerging technological and critical moment.
culturalanalytics.org
October 13, 2025 at 3:50 PM
heuser.bsky.social
Finally, I speculate whether generative AI secretly encodes an aesthetics of its own—as if internalizing Cicero's goals for art to "delight" & "instruct" to an obsessive, historically unprecedented degree. I propose "generative formalism" as a method for studying this hyper-historical aesthetics.
October 13, 2025 at 3:50 PM
heuser.bsky.social
I investigate whether training data can explain this effect, and find some evidence that it cannot. Instead, I find that "instruction-tuning" and Reinforcement Learning Human Feedback—designed to produce "helpful" chatbots—may instill conservative, "idealizing" aesthetic tendencies in the models.
Figure 5.Frequency of rhymed verse in poems detected in open and closed LLM training data. Points indicate mean likelihood; size indicates the number of poems per data point; whiskers show standard error. For comparison, I also reproduce similar results from Walsh, Preus, and Antoniak, who detect poems in both open and closed models by the identical procedure, in their case by querying for a more canonical sample from the Poetry Foundation (PF) and Academy of American Poets (AAP). Then, for all four cases (i.e., Chadwyck-Healey in open and closed datasets; PF/AAP in open and closed datasets), I measure rhyme in the poems discovered by these methods to be present or absent in the training data (Figure 5). Across all methods and datasets, the distribution of rhyme in poems found in LLM training data (shown in blue) is not disproportionately high compared to poems not found in that data (shown in red). A comparison of means between found and unfound poems’ rhyming shows in all cases either statistically insignificant differences or differences with small effect sizes. Moreover, though very few poems from the historical corpora used in this study were found in open LLM training data, those found even rhymed less often than those not found. These results suggest that LLMs’ tendency to overuse rhyme cannot be explained simply by an overrepresentation of rhyming poetry in their training data. Rather, this data points to something more fundamental in how these models process and generate poetic forms. Figure 9.Frequency of rhymed verse across generative completions of historical poems, one “instruction-tuned” and the other a raw next token generator. Points indicate mean likelihood; size indicates the number of poems per data point; whiskers show standard error. Figure 9 above shows that llama3.1:instruct rhymes far more often than its identically-trained llama3.1:text. Comparing the frequency of rhymed verse in these two variants of the same model shows that the raw text completion variant rhymes far less (with a statistically significant difference of means across all historical periods of the completed poems) than the one tuned to respond helpfully to user instructions. Does this helpful responsiveness push them, by accident, into more idealized aesthetic forms like rhythm and regular rhythm?
October 13, 2025 at 3:50 PM
heuser.bsky.social
As in work by @mellymeldubs.bsky.social et al, I examine formal tendencies in the poetic output of 9 LLMs. I find that LLM verse is "formally stuck" on rhyme & regular meter, producing these forms far more than even the formally strictest periods of literary history—and even when instructed not to.
Figure 1.Frequency of rhymed poems in the Chadwyck-Healey corpora compared with LLM-generated verse. Generative models were prompted for three types of poems: rhyming poems, unrhyming poems, and poems without specifying whether to rhyme. Points indicate mean likelihood; size indicates the number of poems per data point; whiskers show standard error. The left-hand portion of the graph shows that historically, across the 1,000 sampled poems per half-century in the Chadwyck-Healey poetry collections, the average likelihood for a poem to rhyme descends over time, particularly since the late nineteenth century. Prior to that, the historical incidence of rhymed verse hovers between 71% and 90% for three centuries, before turning away from it in the late nineteenth century (71%) and plummeting in the early twentieth (15%)—with ultimately only 7% of poems written by postwar poets in rhyme. Meanwhile, the right-hand portion of the graph, a distant reading of rhyme not in historical but generative verse, shows several striking findings. On average across the LLMs, prompts for a rhyming poem (shown in blue) and prompts simply for a poem without specifying whether to rhyme (shown in green) produce a body of artificial verse composed of 95% rhyming poetry. Not only is this incidence more than in any historical period of actual human-authored verse, but its similarity across these two categories of prompts suggests a remarkably stubborn association between the genre of poetry and the form of rhyme within these models. Furthermore, whether prompted simply to write a poem, a rhyming poem, or an unrhyming poem (shown in purple), generative models—despite the overwhelming bias in their training data toward the contemporary—rhyme more often than poets born in the late twentieth century, and sometimes significantly more. Finally, even prompting the models for an unrhyming poem yields rhyming poems 50% of the time on average across the models, more than seven times as often as in the … Figure 6.Frequency of syllable stress per syllable position into 10-syllable lines, drawn from historical and generative sonnets. Points indicate mean likelihood; whiskers indicate standard error. Even before attempting to metrically scan these syllable stresses, Figure 6 shows how generative sonnets arrange their unstressed and stressed syllables more regularly than in any known century of the form. That LLM imitations of Shakespeare’s sonnets (in red) dip much lower in syllable stress likelihood in the odd-numbered syllables and peak much higher in the even-numbered ones—more than any other sonnet source, including Shakespeare’s own—shows how inhumanly rigidly its syllable stresses adhere to an iambic pentameter metrical template. The second syllable in the line, for example, is stressed 62% of the time in twentieth-century sonnets; 63% Shakespeare; and 69% in pre-twentieth-century sonnets, partly given the more frequent use of traditional metrical variations like the trochaic inversion. But LLM verse stresses this syllable a striking 89% of the time, far exceeding the sonnets of any historical period from the seventeenth through the twentieth centuries. Although this syllable shows the largest difference between generative and historical sonnet meter, and although all sonnet sources rise in regularity toward the end of the line (a well-known phenomenon in metrics), generative sonnets maintain an historically unprecedented regularity in syllable stress across the line.
October 13, 2025 at 3:50 PM