EcomGPT: Instruction-Tuning Large Language Models with Chain-of-Task Tasks for E-commerce
Authors: Yangning Li, Shirong Ma, Xiaobin Wang, Shen Huang, Chengyue Jiang, Hai-Tao Zheng, Pengjun Xie, Fei Huang, Yong Jiang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and human evaluations demonstrate that Ecom GPT outperforms Chat GPT in term of cross-dataset/task generalization on E-commerce tasks. |
| Researcher Affiliation | Collaboration | 1SIGS, Tsinghua University 2Shanghai Tech University 3DAMO Academy, Alibaba Group 4Peng Cheng Laboratory |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | The Ecom GPT will be public at https://github.com/Alibaba-NLP/Ecom GPT. |
| Open Datasets | Yes | We manually collected a wide range of E-commerce natural language processing (NLP) datasets from open data sources, such as academic websites and data competition platforms. |
| Dataset Splits | No | The Ecom Instruct dataset is divided into two partitions, namely training and testing." The paper specifies training and testing splits but does not explicitly provide details about a separate validation dataset split. |
| Hardware Specification | Yes | All experiments are run on 4 NVIDIA A100 SXM4 80GB GPUs. |
| Software Dependencies | No | The paper mentions the use of 'Adam W' optimizer and refers to models like 'BLOOMZ' and 'Chat GPT', and a tool 'Alpaca Garbage Collector' (with a URL), but it does not specify explicit version numbers for programming languages, libraries, or frameworks (e.g., Python version, PyTorch/TensorFlow version, CUDA version) needed for replication. |
| Experiment Setup | Yes | Adam W (Loshchilov and Hutter 2017) optimizer is employed for model training, with learning rate set of 2e-5 and weight decay of 0. We utilize a cosine learning rate schedule, warming up over 3% of the training steps. The model is fine-tuned with 3 epochs, with the batch size per device set to 4 and the gradient accumulation step set to 8. The maximum sequence length is 1024. |