Feb. 27, 2024, 5:49 a.m. | Jungyeul Park, Mengyang Qiu

cs.CL updates on arXiv.org arxiv.org

arXiv:2402.15931v1 Announce Type: new
Abstract: This paper introduces a novel perspective on the automated essay scoring (AES) task, challenging the conventional view of the ASAP dataset as a static entity. Employing simple text denoising techniques using prompting, we explore the dynamic potential within the dataset. While acknowledging the previous emphasis on building regression systems, our paper underscores how making minor changes to a dataset through text denoising can enhance the final results.

abstract arxiv automated building cs.cl dataset denoising dynamic essay explore novel paper perspective prompting regression scoring simple systems text type view

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Data Engineer

@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Lead Developer (AI)

@ Cere Network | San Francisco, US