REVIEW 2 cited by
Navigating the Path of Writing: Outline-guided Text Generation with Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Large Language Models (LLMs) have impacted the writing process, enhancing productivity by collaborating with humans in content creation platforms. However, generating high-quality, user-aligned text to satisfy real-world content creation needs remains challenging. We propose WritingPath, a framework that uses explicit outlines to guide LLMs in generating goal-oriented, high-quality text. Our approach draws inspiration from structured writing planning and reasoning paths, focusing on reflecting user intentions throughout the writing process. To validate our approach in real-world scenarios, we construct a diverse dataset from unstructured blog posts to benchmark writing performance and introduce a comprehensive evaluation framework assessing the quality of outlines and generated texts. Our evaluations with various LLMs demonstrate that the WritingPath approach significantly enhances text quality according to evaluations by both LLMs and professional writers.
Forward citations
Cited by 2 Pith papers
-
Segment-Level Diffusion: A Framework for Controllable Long-Form Generation with Diffusion Language Models
Segment-Level Diffusion generates long-form text by planning per-segment latent representations with a diffusion transformer and decoding them in parallel.
-
From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go
Adding LLM-based cleanup and an edit-by-prompt agent to a voice-based review app raised users' willingness to share reviews and their self-reported confidence in a 14-participant field study.
Discussion (0). Continue with ORCID to comment.