Latent Logic Tree Extraction for Event Sequence Explanation from LLMs
Authors: Zitao Song, Chao Yang, Chaojie Wang, Bo An, Shuang Li
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical demonstrations showcase the promising performance and adaptability of our framework. Empirical results show that this method notably enhances generalization in event histories with semantic information. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Engineering, Nanyang Technological University, Singapore 2School of Data Science, The Chinese University of Hong Kong, Shenzhen, China 3Skywork AI, Singapore. |
| Pseudocode | Yes | Algorithm 1 Bayesian Logic Tree Learning for Events |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | MIMIC-III (Johnson et al., 2016), an electronic health record dataset from intensive care unit patients. ... EPIC-KITCHENS-100 (EPIC-100) (Damen et al., 2021), which documents everyday kitchen activities... Stack Overflow (SO) (Leskovec & Krevl, 2014), which records a sequence of reward history... |
| Dataset Splits | Yes | We consider each sequence as a record pertaining to a single individual and partition each dataset into 80%, 10%, 10% train/dev/test splits by the total population. |
| Hardware Specification | Yes | All the experiments were conducted on a server with 512G RAM, two 64 logical cores CPUS (AMD Ryzen Threadripper PRO 5995WX 64-Cores), and four NVIDIA RTX A6000 GPUs with 50G memory. |
| Software Dependencies | No | The paper states 'All models are implemented using the Py Torch framework.' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We present the selected hyperparameters on synthetic datasets and three real-world datasets in Table 6 and Table 7 respectively. These tables list specific values for 'EPOCHS', 'BATCH SIZE', 'LLM LR', 'LOGIC TREE DEPTH', 'LOGIC TREE WIDTH', and other training parameters. |