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..
Text Revision By On-the-Fly Representation Optimization
Authors: Jingjing Li, Zichao Li, Tao Ge, Irwin King, Michael R. Lyu10956-10964
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical experiments on two typical and important text revision tasks, text formalization and text simplification, show the effectiveness of our approach. |
| Researcher Affiliation | Collaboration | Jingjing Li1, Zichao Li2, Tao Ge3, Irwin King1, Michael R. Lyu1 1The Chinese University of Hong Kong 2Mila/Mc Gill University 3Microsoft Research Asia |
| Pseudocode | Yes | Algorithm 1: Text revision with OREO |
| Open Source Code | Yes | Our code and model are released at https://github.com/jingjingli01/OREO. |
| Open Datasets | Yes | Based on the widely used corpora Newsela (Xu, Callison-Burch, and Napoles 2015), Jiang et al. (2020) constructs a reliable corpus consisting of 666K complex-simple sentence pairs1. 1Dataset available at https://github.com/chaojiang06/wiki-auto. ... We experimented with the domain of Family & Relationships in Grammarly s Yahoo Answers Formality Corpus (GYAFC-fr) (Rao and Tetreault 2018). |
| Dataset Splits | Yes | The final dataset consists of 269K train, 28K development and 29K test sentences. ... There are 100K, 5K and 2.5K informal-formal2 pairs in GYAFC. |
| Hardware Specification | Yes | It takes 8-GPU hours to fine-tune Ro BERTa on one Tesla V100 for both tasks. |
| Software Dependencies | No | The paper states, 'We implement Ro BERTa based on Huggingface transformers (Wolf et al. 2020).' While it names a library and cites a paper for it, it does not provide specific version numbers for the software dependencies needed for replication. |
| Experiment Setup | Yes | We primarily adopted the default hyperparameters with a fixed learning rate of 5e-5. The numbers of fine-tuning epochs are 6 and 2 for text simplification and formalization, respectively. ... The maximum iteration I was set to 4... λ was selected from {0.8, 1.2, 1.6, 2.0} and set to 1.6. ... The attribute threshold δ is task-dependent. It was selected from from {0.1, 0.2, . . . , 0.5} and set to 0.5 for text simplification and 0.3 for text formalization. K = 1 for both tasks. |