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..
Meta-Diffu$B$: A Contextualized Sequence-to-Sequence Text Diffusion Model with Meta-Exploration
Authors: Yun-Yen Chuang, Hung-Min Hsu, Kevin Lin, Chen-Sheng Gu, Ling-Zhen Li, Ray-I Chang, Hung-yi Lee
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct experiments to verify the performance of our Meta-Diffu B on four benchmark Seq2Seq datasets [48, 6, 17, 8]. |
| Researcher Affiliation | Collaboration | Yun-Yen Chuang1,2, Hung-Min Hsu3, Kevin Lin4, Chen-Sheng Gu1,2, Ling Zhen Li1,2, Ray-I Chang2, Hung-yi Lee2 1Maxora AI 2National Taiwan University 3University of Washington 4Microsoft |
| Pseudocode | Yes | Algorithm 1 Meta-Diffu B |
| Open Source Code | Yes | 1Code and datasets for Meta-Diffu B are available at: https://github.com/Meta-Diffu B/ Meta-Diffu B. |
| Open Datasets | Yes | In our experiment, we use four datasets: the Commonsense Conversation dataset (CC) [48], the Quasar-T dataset (QT) [6], the Wiki-Auto dataset (WA) [17], and the Quora Question Pairs dataset (QQP) [8]. |
| Dataset Splits | Yes | The training set contains 3,382,137 pairs, the development set has 2,048, and the test set includes 10,000 pairs. |
| Hardware Specification | Yes | Experiments are conducted on NVIDIA A100 Tensor Core GPUs, utilizing 4 GPUs for training and a single GPU for inference. |
| Software Dependencies | No | The paper mentions general software components like 'Transformer model' and 'LSTM' but does not provide specific version numbers for any libraries or dependencies. |
| Experiment Setup | Yes | The diffusion step count is set at 2,000, and the maximum sequence length is 128. The Minimum Bayes risk (MBR) [23] decoding size, denoted as |S|, is 10; this involves generating sentences from 10 random seeds and selecting the best output sequence. The total batch size for both training and testing phases is 2048. |