XATU: A Fine-grained Instruction-based Benchmark for Explainable Text Updates

TL;DR

XATU is a new benchmark for fine-grained, explainable text editing tasks that evaluates large language models' capabilities across various editing challenges with interpretability.

Contribution

This paper introduces XATU, the first benchmark focusing on fine-grained, explainable text editing with diverse tasks and combined annotation methods for interpretability.

Findings

Instruction tuning improves editing performance.

Model architecture significantly affects results.

Explanations enhance model fine-tuning for text editing.

Abstract

Text editing is a crucial task of modifying text to better align with user intents. However, existing text editing benchmark datasets contain only coarse-grained instructions and lack explainability, thus resulting in outputs that deviate from the intended changes outlined in the gold reference. To comprehensively investigate the text editing capabilities of large language models (LLMs), this paper introduces XATU, the first benchmark specifically designed for fine-grained instruction-based explainable text editing. XATU considers finer-grained text editing tasks of varying difficulty (simplification, grammar check, fact-check, etc.), incorporating lexical, syntactic, semantic, and knowledge-intensive edit aspects. To enhance interpretability, we combine LLM-based annotation and human annotation, resulting in a benchmark that includes fine-grained instructions and gold-standard edit explanations. By evaluating existing LLMs against our benchmark, we demonstrate the effectiveness of instruction tuning and the impact of underlying architecture across various editing tasks. Furthermore, extensive experimentation reveals the significant role of explanations in fine-tuning language models for text editing tasks. The benchmark will be open-sourced to support reproduction and facilitate future research at~\url{https://github.com/megagonlabs/xatu}.