all AI news
Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency
Feb. 27, 2024, 5:49 a.m. | Min Zeng, Jiexin Kuang, Mengyang Qiu, Jayoung Song, Jungyeul Park
cs.CL updates on arXiv.org arxiv.org
Abstract: The writing examples of English language learners may be different from those of native speakers. Given that there is a significant differences in second language (L2) learners' error types by their proficiency levels, this paper attempts to reduce overcorrection by examining the interaction between LLM's performance and L2 language proficiency. Our method focuses on zero-shot and few-shot prompting and fine-tuning models for GEC for learners of English as a foreign language based on the different …
abstract arxiv cs.cl differences english english language error error correction examples language paper prompting reduce speakers strategies type types writing
More from arxiv.org / cs.CL updates on arXiv.org
Jobs in AI, ML, Big Data
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