logo
Loading...
Improving Web Element Localization by Using a Large Language Model

Computer Science

Improving Web Element Localization by Using a Large Language Model

M. Nass, E. Alégroth, et al.

VON Similo LLM enhances web element localization by using an LLM to select the most likely element from top candidates identified by VON Similo. In an empirical study on 804 element pairs it reduced failed localizations from 70 to 40 (43% reduction), though with slower execution and added GPT-4 cost. Research conducted by Michel Nass, Emil Alégroth, and Robert Feldt.... show more
Citation Metrics
Citations
10
Influential Citations
2
Reference Count
64
Citation by Year

Note: The citation metrics presented here have been sourced from Semantic Scholar and OpenAlex.

Related Publications

Explore these studies to deepen your understanding of the subject.

A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing
Engineering and Technology

A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing

P. Shetty, A. C. Rajan, et al.

A scoping review of large language model based approaches for information extraction from radiology reports
Medicine and Health

A scoping review of large language model based approaches for information extraction from radiology reports

D. Reichenpfader, H. Müller, et al.

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets
Medicine and Health

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets

X. Xue, S. Chinnaperumal, et al.

Predictive model of castration resistance in advanced prostate cancer by machine learning using genetic and clinical data: KYUCOG-1401-A study
Medicine and Health

Predictive model of castration resistance in advanced prostate cancer by machine learning using genetic and clinical data: KYUCOG-1401-A study

M. Shiota, S. Nemoto, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 22+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny