Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
ParaGuide: Guided Diffusion Paraphrasers for Plug-and-Play Textual Style Transfer
Authors: Zachary Horvitz, Ajay Patel, Chris Callison-Burch, Zhou Yu, Kathleen McKeown
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the method on the Enron Email Corpus, with both human and automatic evaluations, and find that it outperforms strong baselines on formality, sentiment, and even authorship style transfer. |
| Researcher Affiliation | Academia | 1 Columbia University 2 University of Pennsylvania |
| Pseudocode | Yes | Algorithm 1: Para Guide Style Transfer |
| Open Source Code | Yes | Our code is publicly available at https://github.com/ zacharyhorvitz/Para Guide. |
| Open Datasets | Yes | We evaluate our method on the Enron Email Corpus, which comprises several hundred thousand emails made public during the US government s investigation of Enron (Klimt and Yang 2004; Peterson, Hohensee, and Xia 2011). ... In addition to the Enron corpus, we also build a pretraining corpus from the Reddit Million User Dataset (MUD) (Andrews and Bishop 2019; Khan et al. 2021) |
| Dataset Splits | Yes | To build our training and validation datasets for attribute style transfer, we use popular existing formality and sentiment classifiers to score texts from the holdout authors in the Enron dataset. |
| Hardware Specification | No | The paper states that the method "can be fine-tuned on a single GPU" but does not specify the model or type of GPU, or any other hardware components. |
| Software Dependencies | No | The paper mentions software like SSD-LM RoBERTa-Large, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | PARAGUIDE s complete inference procedure is specified in Algorithm 1. ... We additionally visualize the affect of varying λ on authorship style transfer in Figure 2. When λ is small, the paraphrase-conditioned diffusion model reconstructs a more semantically faithful, fluent output. However, we can increase λ to improve Confusion scores, at the cost of semantic consistency and fluency. At the lowest setting, PARAGUIDE s Fluency and Similarity score are similar to those of Chat GPT-3.5 (0.78 vs 0.79 and 0.52 vs 0.56). |