CoEdIT: Text Editing by Task-Specific Instruction Tuning
원문 초록
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 large language model 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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Assay2Mol: large language model-based drug design using BioAssay context
Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery
Detecting Referring Expressions in Visually Grounded Dialogue with Autoregressive Language Models
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
AgentStealth: Reinforcing Large Language Model for Anonymizing User-generated Text
A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection
토론
이 논문과 연결된 포럼 글이 아직 없습니다.