1 citations · 1 across the 3 of their papers we have counts for
4 papers
Larger Context Window, Fewer Overcorrections: Optimizing Prompts and Batching for Minimal-Edit Grammatical Error Correction
Kateryna Karpo, Artem Chernodub
Minimal-edit Grammatical Error Correction (GEC) is a challenging task for zero- and few-shot prompted Large Language Models (LLMs), which systematically overcorrect and degrade $F_…
How Far Can Prompting Go for Minimal-Edit Ukrainian Grammatical Error Correction?
Kateryna Karpo, Artem Chernodub
Fine-tuned Large Language Models (LLMs) dominate in Ukrainian grammatical error correction (GEC), while API-accessed LLMs remain nearly untested on minimal-edit benchmarks. We eval…
APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification
Artem Chernodub, Aman Saini, Yejin Huh +2
Recent advancements in large language models (LLMs) have enabled a wide range of natural language processing (NLP) tasks to be performed through simple prompt-based interactions. C…
Spivavtor: An Instruction Tuned Ukrainian Text Editing Model
Aman Saini, Artem Chernodub, Vipul Raheja +1
We introduce Spivavtor, a dataset, and instruction-tuned models for text editing focused on the Ukrainian language. Spivavtor is the Ukrainian-focused adaptation of the English-onl…