Copertina

Surprisal reveals diversity gaps in image captioning and different scorers change the story

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ilinykh, Nikolai, Dobnik, Simon
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computation and Language
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Coreference as an indicator of context scope in multimodal narrative
di: Ilinykh, Nikolai, et al.
Pubblicazione: (2025)

Image captioning in different languages
di: van Miltenburg, Emiel
Pubblicazione: (2024)

Humans vs Vision-Language Models: A Unified Measure of Narrative Coherence
di: Ilinykh, Nikolai, et al.
Pubblicazione: (2026)

Predicting Sentence Acceptability Judgments in Multimodal Contexts
di: Jang, Hyewon, et al.
Pubblicazione: (2026)

Attention-based transformer models for image captioning across languages: An in-depth survey and evaluation
di: Albadarneh, Israa A., et al.
Pubblicazione: (2025)

Image captioning for Brazilian Portuguese using GRIT model
di: de Alencar, Rafael Silva, et al.
Pubblicazione: (2024)

Clozing the Gap: Exploring Why Language Model Surprisal Outperforms Cloze Surprisal
di: Nair, Sathvik, et al.
Pubblicazione: (2026)

Whose story is it? Personalizing story generation by inferring author styles
di: Kumar, Nischal Ashok, et al.
Pubblicazione: (2025)

Surprise! Uniform Information Density Isn't the Whole Story: Predicting Surprisal Contours in Long-form Discourse
di: Tsipidi, Eleftheria, et al.
Pubblicazione: (2024)

Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data
di: Zhang, Chenhui, et al.
Pubblicazione: (2024)

On the Proper Treatment of Units in Surprisal Theory
di: Kiegeland, Samuel, et al.
Pubblicazione: (2026)

Towards a Similarity-adjusted Surprisal Theory
di: Meister, Clara, et al.
Pubblicazione: (2024)

Testing the Predictions of Surprisal Theory in 11 Languages
di: Wilcox, Ethan Gotlieb, et al.
Pubblicazione: (2023)

Expect the Unexpected? Testing the Surprisal of Salient Entities
di: Lin, Jessica, et al.
Pubblicazione: (2026)

Timing is Everything: Temporal Scaffolding of Semantic Surprise in Humor
di: Ma, Yuxi, et al.
Pubblicazione: (2026)

The Frequency Confound in Language-Model Surprisal and Metaphor Novelty
di: Momen, Omar, et al.
Pubblicazione: (2026)

Glitter: Visualizing Lexical Surprisal for Readability in Administrative Texts
di: Černý, Jan, et al.
Pubblicazione: (2026)

Towards a multimodal framework for remote sensing image change retrieval and captioning
di: Ferrod, Roger, et al.
Pubblicazione: (2024)

VCRScore: Image captioning metric based on V\&L Transformers, CLIP, and precision-recall
di: Ruiz, Guillermo, et al.
Pubblicazione: (2025)

The Surprising Effectiveness of Membership Inference with Simple N-Gram Coverage
di: Hallinan, Skyler, et al.
Pubblicazione: (2025)

The Impact of Token Granularity on the Predictive Power of Language Model Surprisal
di: Oh, Byung-Doh, et al.
Pubblicazione: (2024)

Language Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarks
di: Gallifant, Jack, et al.
Pubblicazione: (2024)

The Effect of Surprisal on Reading Times in Information Seeking and Repeated Reading
di: Klein, Keren Gruteke, et al.
Pubblicazione: (2024)

The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning
di: Zhu, Xinyu, et al.
Pubblicazione: (2025)

Surprise Calibration for Better In-Context Learning
di: Tan, Zhihang, et al.
Pubblicazione: (2025)

The exception of humour: Iconicity, Phonemic Surprisal, Memory Recall, and Emotional Associations
di: Kilpatrick, Alexander, et al.
Pubblicazione: (2025)

SPIKE-RL: Video-LLMs meet Bayesian Surprise
di: Ravi, Sahithya, et al.
Pubblicazione: (2025)

Surprisal Minimisation over Goal-directed Alternatives Predicts Production Choice in Dialogue
di: Utting, Tom, et al.
Pubblicazione: (2026)

Confabulation: The Surprising Value of Large Language Model Hallucinations
di: Sui, Peiqi, et al.
Pubblicazione: (2024)

Leveraging image captions for selective whole slide image annotation
di: Qiu, Jingna, et al.
Pubblicazione: (2024)

The Inverse Scaling Effect of Pre-Trained Language Model Surprisal Is Not Due to Data Leakage
di: Oh, Byung-Doh, et al.
Pubblicazione: (2025)

Learning by Surprise: Surplexity for Mitigating Model Collapse in Generative AI
di: Gambetta, Daniele, et al.
Pubblicazione: (2024)

Creating an African American-Sounding TTS: Guidelines, Technical Challenges,and Surprising Evaluations
di: Pinhanez, Claudio, et al.
Pubblicazione: (2024)

FunQA: Towards Surprising Video Comprehension
di: Xie, Binzhu, et al.
Pubblicazione: (2023)

An Existence Proof for Neural Language Models That Can Explain Garden-Path Effects via Surprisal
di: Yoshida, Ryo, et al.
Pubblicazione: (2026)

Learning text-to-video retrieval from image captioning
di: Ventura, Lucas, et al.
Pubblicazione: (2024)

SRS-Stories: Vocabulary-constrained multilingual story generation for language learning
di: Kamzela, Wiktor, et al.
Pubblicazione: (2025)

The Surprising Universality of LLM Outputs: A Real-Time Verification Primitive
di: Bogdan, Alex, et al.
Pubblicazione: (2026)

Surprisal from Larger Transformer-based Language Models Predicts fMRI Data More Poorly
di: Lin, Yi-Chien, et al.
Pubblicazione: (2025)

Calibrated Surprise: An Information-Theoretic Account of Creative Quality
di: Zou, Bo, et al.
Pubblicazione: (2026)