Recent advancements in large language models (LLMs) have enabled a wide range of natural language processing (NLP) tasks through simple prompt-based interactions. Consequently, several approaches have been proposed to engineer prompts that most effectively enable LLMs to perform a given task (e.g., chain-of-thought prompting). In settings with a well-defined metric to optimize model performance, Automatic Prompt Optimization (APO) methods have been developed to refine a seed prompt. Subsequently, we propose APIO, a simple but effective prompt induction and optimization approach for the tasks of Grammatical Error Correction (GEC) and Text Simplification, without relying on manually specified seed prompts. APIO achieves a new state-of-the-art performance for purely LLM-based prompting methods on these tasks. We make our data, code, prompts, and outputs publicly available.

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-only CoEdIT (Raheja et al., 2023) model. Similar to CoEdIT, Spivavtor performs text editing tasks by following instructions in Ukrainian like “Виправте граматику в цьому реченнi” and “Спростiть це речення” which translate to “Correct the grammar in this sentence” and “Simplify this sentence” in English, respectively. This paper describes the details of the Spivavtor-Instruct dataset and Spivavtor models. We evaluate Spivavtor on a variety of text editing tasks in Ukrainian, such as Grammatical Error Correction (GEC), Text Simplification, Coherence, and Paraphrasing, and demonstrate its superior performance on all of them. We publicly release our best performing models and data as resources to the community to advance further research in this space.