Contextualized Scene Imagination for Generative Commonsense Reasoning
Authors: PeiFeng Wang, Jonathan Zamora, Junfeng Liu, Filip Ilievski, Muhao Chen, Xiang Ren
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments demonstrate the effectiveness of I&V in improving language models on both concept-to-sentence and concept-to-story generation tasks, while enabling the model to learn well from fewer task examples and generate SKGs that make common sense to human annotators 1. |
| Researcher Affiliation | Academia | Peifeng Wang1,3, Jonathan Zamora2 , Junfeng Liu1 , Filip Ilievski3, Muhao Chen1,3, Xiang Ren1,3 1Department of Computer Science, University of Southern California 2Department of Computer Science, University of California, San Diego 3Information Sciences Institute, University of Southern California |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data are available at https://github.com/wangpf3/imagine-and-verbalize. |
| Open Datasets | Yes | We evaluate Concept2Sentence on the Common Gen (Lin et al., 2020) benchmark... We construct two benchmarks based on the Visual Story Telling (VIST) (Huang et al., 2016) and ROCStories (Mostafazadeh et al., 2016) datasets. |
| Dataset Splits | Yes | We search the optimal hyper-parameters based on the perplexity over the development set, where the learning rate is chosen from {2e 6, 1e 5, 3e 5, 1e 4}, the batch size is chosen from {8, 16, 32, 64, 128}. |
| Hardware Specification | No | The paper mentions using models like "T5-large" and "BART-large" but does not specify any particular hardware (e.g., GPU model, CPU type, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like "T5-base", "T5-large", "BART-large", and "Adam optimizer" but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We use the Adam optimizer with weight decay 1e 2. We search the optimal hyper-parameters based on the perplexity over the development set, where the learning rate is chosen from {2e 6, 1e 5, 3e 5, 1e 4}, the batch size is chosen from {8, 16, 32, 64, 128}. |