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ARXIV:2602.20918 · MULTIMODAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.20918MULTIMODAL AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Explore how visual contexts affect sentence acceptability judgments by humans and LLMs.
Opportunity summary
Pain Explore how visual contexts affect sentence acceptability judgments by humans and LLMs.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Explore how visual contexts affect sentence acceptability judgments by humans and LLMs. We consider the effect of prior exposure to visual images (i.e., visual context) on these judgments for humans and large language models…
Previous work has examined the capacity of deep neural networks (DNNs), particularly transformers, to predict human sentence acceptability judgments, both independently of context, and in document contexts. We consider the effect of prior exposure…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Our results suggest that, in contrast to textual context, visual images appear to have little if any impact on human acceptability ratings.
Multimodal AI moved forward this cycle; last verified April 2026. Public score 3.0/10.
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Explore how visual contexts affect sentence acceptability judgments by humans and LLMs.
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10.48550/arXiv.2602.20918Explore how visual contexts affect sentence acceptability judgments by humans and LLMs.
Abstract
Previous work has examined the capacity of deep neural networks (DNNs), particularly transformers, to predict human sentence acceptability judgments, both independently of context, and in document contexts. We consider the effect of prior exposure to visual images (i.e., visual context) on these judgments for humans and large language models (LLMs). Our results suggest that, in contrast to textual context, visual images appear to have little if any impact on human acceptability ratings. However, LLMs display the compression effect seen in previous work on human judgments in document contexts. Different sorts of LLMs are able to predict human acceptability judgments to a high degree of accuracy, but in general, their performance is slightly better when visual contexts are removed. Moreover, the distribution of LLM judgments varies among models, with Qwen resembling human patterns, and others diverging from them. LLM-generated predictions on sentence acceptability are highly correlated with their normalised log probabilities in general. However, the correlations decrease when visual contexts are present, suggesting that a higher gap exists between the internal representations of LLMs and their generated predictions in the presence of visual contexts. Our experimental work suggests interesting points of similarity and of difference between human and LLM processing of sentences in multimodal contexts.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 3.0
PROBLEM
Explore how visual contexts affect sentence acceptability judgments by humans and LLMs. We consider the effect of prior exposure to visual images (i.e., visual context) on these judgments for humans and large language models (LLMs).
METHOD
Previous work has examined the capacity of deep neural networks (DNNs), particularly transformers, to predict human sentence acceptability judgments, both independently of context, and in document contexts. We consider the effect of prior exposure to visual images (i.e., visual...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Our results suggest that, in contrast to textual context, visual images appear to have little if any impact on human acceptability ratings.
WHY NOW
Multimodal AI moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Explore how visual contexts affect sentence acceptability judgments by humans and LLMs. We consider the effect of prior exposure to visual images (i.e., visual context) on these judgments for humans and large language models (LLMs).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Previous work has examined the capacity of deep neural networks (DNNs), particularly transformers, to predict human sentence acceptability judgments, both independently of context, and in document contexts. We consider the effect of prior exposure to visual images (i.e., visual context) on these judgments for humans and large language models (LLMs).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Our results suggest that, in contrast to textual context, visual images appear to have little if any impact on human acceptability ratings.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multimodal AI moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Explore how visual contexts affect sentence acceptability judgments by humans and LLMs.
Segment
Multimodal AI
Adoption evidence
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Commercial read
3.0/10 public viability
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reason
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proof status
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next verification path
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partial
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Run minimal reproduction from the Build Passport prototype path.
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Write integration checklist from prototype path and target workflow.
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