Did AI get more negative recently?

Beese, Dominik; Begüm, Altunbaş,; Güzeler, Görkem; Eger, Steffen

doi:10.1098/rsos.221159

Cited by 1 publication

(2 citation statements)

References 63 publications

(100 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use the dataset released by Beese et al (2023), which contains title-abstract pairs and corresponding meta-information such as the publication year and venue. Beese et al (2023) extracted the data from two sources: ACL Anthology (from 1984 to 2021) and machine learning conferences (from 1989 to 2021); we refer to the datasets from these two sources as NLP and ML, respectively. After filtering (described in Appendix A), 32,952 abstracttitle pairs remain in our dataset.…”

Section: Datamentioning

confidence: 99%

“…Stage 2: To find more funny title candidates to annotate, the two annotators annotated the funniest 396 titles in the original dataset from Beese et al (2023), predicted by the Stage 1 ensemble classifier; 75.8% (300 titles) were judged as FUNNY or FUNNY med , which is substantially higher than the proportion of funny titles in the annotated data of Stage 1 (7.3%). Thus, the annotated data expands to 2,441 titles (= 1, 730 + 315 + 396), where 1,893 are labeled as ¬FUNNY, 492 as FUNNY med and 56 as FUNNY.…”

Section: Humorous Title Generationmentioning

confidence: 99%

See 1 more Smart Citation

Transformers Go for the LOLs: Generating (Humourous) Titles from Scientific Abstracts End-to-End

Chen,

Eger

2023

Proceedings of the 4th Workshop on Evaluation and Comparison of NLP Systems

View full text Add to dashboard Cite

We consider the end-to-end abstract-to-title generation problem, exploring seven recent transformer based models (including ChatGPT) fine-tuned on more than 30k abstract-title pairs from NLP and machine learning (ML) venues. As an extension, we also consider the harder problem of generating humorous paper titles. For the latter, we compile the first large-scale humor annotated dataset for scientific papers in the NLP/ML domains, comprising ∼2.6k titles. We evaluate all models using human and automatic metrics. Our human evaluation suggests that our best end-to-end system performs similarly to human authors (but arguably slightly worse). Generating funny titles is more difficult, however, and our automatic systems clearly underperform relative to humans and often learn dataset artefacts of humor. Finally, ChatGPT, without any fine-tuning, performs on the level of our best fine-tuned system. 1

show abstract

Section: Datamentioning

confidence: 99%

Section: Humorous Title Generationmentioning

confidence: 99%