Arkadiy Saakyan
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asaakyan.bsky.social
Arkadiy Saakyan
@asaakyan.bsky.social
420 followers 210 following 16 posts
PhD student at Columbia University working on human-AI collaboration, AI creativity and explainability. prev. intern @GoogleDeepMind, @AmazonScience
asaakyan.github.io
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asaakyan.bsky.social
Arkadiy Saakyan @asaakyan.bsky.social · 9d
N-gram novelty is widely used as a measure of creativity and generalization. But if LLMs produce highly n-gram novel expressions that don’t make sense or sound awkward, should they still be called creative? In a new paper, we investigate how n-gram novelty relates to creativity.
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 7542 expert writer annotations (n=26) 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, ~91% of top-quartile expressions by n-gram novelty are not judged as creative, cautioning against relying on n-gram novelty alone. Furthermore, unlike human-written text, higher n-gram novelty in open-source LLMs correlates with lower pragmaticality. In an exploratory study with frontier close-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 creative expressions (a positive aspect of writing) and non-pragmatic ones (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 scores from the best-performing model were predictive of expert writer preferences.
Reposted by Arkadiy Saakyan
urban-data.bsky.social
Day 7 of #30DayMapChallenge asked us to think about accessibility. @gsagostini.bsky.social considers two metrics of access simultaneously: distance to a Subway and distance to the subway.
November 13, 2025 at 2:27 PM
asaakyan.bsky.social
N-gram novelty is widely used as a measure of creativity and generalization. But if LLMs produce highly n-gram novel expressions that don’t make sense or sound awkward, should they still be called creative? In a new paper, we investigate how n-gram novelty relates to creativity.
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 7542 expert writer annotations (n=26) 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, ~91% of top-quartile expressions by n-gram novelty are not judged as creative, cautioning against relying on n-gram novelty alone. Furthermore, unlike human-written text, higher n-gram novelty in open-source LLMs correlates with lower pragmaticality. In an exploratory study with frontier close-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 creative expressions (a positive aspect of writing) and non-pragmatic ones (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 scores from the best-performing model were predictive of expert writer preferences.
November 4, 2025 at 3:08 PM
Reposted by Arkadiy Saakyan
gsagostini.bsky.social
Are you a researcher using computational methods to understand cities?

@mfranchi.bsky.social @jennahgosciak.bsky.social and I organize an EAAMO Bridges working group on Urban Data Science and we are looking for new members!

Fill the interest form on our page: urban-data-science-eaamo.github.io
Urban Data Science & Equitable Cities | EAAMO Bridges
EAAMO Bridges Urban Data Science & Equitable Cities working group: biweekly talks, paper studies, and workshops on computational urban data analysis to explore and address inequities.
urban-data-science-eaamo.github.io
September 3, 2025 at 3:05 PM
Reposted by Arkadiy Saakyan
danielsc4.it
📢 New paper: Applied interpretability 🤝 MT personalization!

We steer LLM generations to mimic human translator styles on literary novels in 7 languages. 📚

SAE steering can beat few-shot prompting, leading to better personalization while maintaining quality.

🧵1/
May 23, 2025 at 12:23 PM
asaakyan.bsky.social
Can vision-language models understand figurative meaning in multimodal inputs, like visual metaphors, sarcastic captions or memes? Come find out at our #NAACL2025 poster on Friday at 9am!

New task & dataset of images and captions with figurative phenomena like metaphor, idiom, sarcasm, and humor.
May 1, 2025 at 4:30 PM
Reposted by Arkadiy Saakyan
gsagostini.bsky.social
Migration data lets us study responses to environmental disasters, social change patterns, policy impacts, etc. But public data is too coarse, obscuring these important phenomena!

We build MIGRATE: a dataset of yearly flows between 47 billion pairs of US Census Block Groups. 1/5
March 28, 2025 at 3:25 PM
Reposted by Arkadiy Saakyan
jennarussell.bsky.social
People often claim they know when ChatGPT wrote something, but are they as accurate as they think?

Turns out that while general population is unreliable, those who frequently use ChatGPT for writing tasks can spot even "humanized" AI-generated text with near-perfect accuracy 🎯
January 28, 2025 at 2:55 PM