CoEdIT: Text Editing by Task-Specific Instruction Tuning (2305.09857v2)

Abstract: We introduce CoEdIT, a state-of-the-art text editing system for writing assistance. CoEdIT takes instructions from the user specifying the attributes of the desired text, such as "Make the sentence simpler" or "Write it in a more neutral style," and outputs the edited text. We present a LLM fine-tuned on a diverse collection of task-specific instructions for text editing (a total of 82K instructions). Our model (1) achieves state-of-the-art performance on various text editing benchmarks, (2) is competitive with publicly available largest-sized LLMs trained on instructions while being nearly 60x smaller, (3) is capable of generalizing to unseen edit instructions, and (4) exhibits abilities to generalize to composite instructions containing different combinations of edit actions. Through extensive qualitative and quantitative analysis, we show that writers prefer the edits suggested by CoEdIT relative to other state-of-the-art text editing models. Our code, data, and models are publicly available at https://github.com/vipulraheja/coedit.

• The paper introduces a task-specific instruction tuning approach that achieves state-of-the-art performance in text editing tasks like grammatical error correction, simplification, and stylistic adjustments.
• The paper leverages fine-tuning on 82K diverse instructions, enabling robust generalization to unseen and composite editing commands.
• The paper demonstrates compelling efficiency and human preference for its edits, showcasing a model 60x smaller than conventional LLMs while excelling in automated writing assistance.

An Insightful Overview of "CoEdIT: Text Editing by Task-Specific Instruction Tuning"

The paper "CoEdIT: Text Editing by Task-Specific Instruction Tuning" presents CoEdIT, a text editing system developed to enhance writing assistance through task-focused instruction tuning of LLMs. CoEdIT operates by taking user-provided instructions such as "Make the sentence simpler" or "Paraphrase the text," effectively editing the input text according to these directives. This approach leverages a LLM fine-tuned on 82,000 diverse task-specific instructions for text editing, demonstrating notable achievements in both performance and efficiency.

Contributions and Findings

The paper identifies four primary contributions:

1. State-of-the-Art Performance: CoEdIT achieves superior results across multiple text editing tasks, including grammatical error correction (GEC), text simplification, and various stylistic edits like formality modification, neutralization, and paraphrasing.
2. Model Efficiency: The system is competitive with the most extensive publicly available LLMs trained on instructions yet is significantly smaller, by approximately 60 times.
3. Generalization Capabilities: CoEdIT shows a robust ability to generalize to unseen tasks and composite instructions, demonstrating versatility in handling novel combinations of text editing operations.
4. Human Preference: Through rigorous qualitative and quantitative evaluations, CoEdIT's suggested edits are often preferred by writers over those from existing text editing models.

Detailed Examination

CoEdIT's advancement lies in its employment of task-specific instruction tuning, which diverges from previous approaches focused on either general-purpose tuning or small-scale datasets. By honing the model on a rich and varied set of edit tasks articulated in natural language, the paper effectively shows that this method enhances the model's understanding and execution of textual instructions, thereby reducing the reliance on few-shot exemplars.

Experimental Setup and Evaluation

The paper conducts an exhaustive battery of quantitative experiments across a variety of benchmarks, including JFLEG for fluency, TurkCorpus and ASSET for text simplification, and specialized datasets for style-related edits like the Grammarly's Yahoo Answers Formality Corpus (GYAFC). CoEdIT consistently outperforms not only a baseline model and other instruction-tuned LLMs but also current state-of-the-art text editing models across nearly all metrics.

Generalizability and Composite Instructions

Beyond single-task instruction adherence, CoEdIT adeptly handles composite tasks, showcasing superior performance on combined directives like simplifying and paraphrasing simultaneously. This is tested through both internal evaluations using the CoEdIT dataset and human assessments. The research provides compelling insights into the model's robustness under various textual modifications and encourages future exploration of blending multiple editing tasks seamlessly.

Implications and Future Directions

The implications of CoEdIT's development are extensive, offering a leap in automated writing assistance applicable in educational technology, content creation, and professional writing enhancement. It addresses several fundamental challenges associated with linguistic diversity in editing tasks and showcases the potential for models that are agile yet efficient.

The researchers propose that their task-specific instruction-tuned approach may serve as a benchmark for future developments where task density and instructional diversity could offer even more nuanced control over text manipulation applications.

Conclusion

Overall, "CoEdIT: Text Editing by Task-Specific Instruction Tuning" delineates a significant stride in text editing capabilities, leveraging task-specific instruction enhancement to equip LLMs with refined editing aptitudes. By achieving remarkable results with reduced model size, CoEdIT provides a strong case for adopting focused instruction sets in LLM development, inviting further innovation in this domain. This paper sets a precedent for streamlined yet powerful text editing solutions in AI and underscores a robust future direction for task-specific natural language processing models.