Evolutionary Large Language Model for Automated Feature Transformation
Authors: Nanxu Gong, Chandan K Reddy, Wangyang Ying, Haifeng Chen, Yanjie Fu
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically demonstrate the effectiveness and generality of our proposed method. ... Our experimental results demonstrate the effectiveness and robustness of ELLM-FT. ... Table 1 shows the detailed statistics of the data sets. We adopted Random Forest (RF) as the downstream model. We used the F-1 score to measure the accuracy of classification tasks, and use the 1 relative absolute error (RAE) to measure the accuracy of regression tasks. We performed 5-fold stratified cross-validation to reduce random errors in experiments. |
| Researcher Affiliation | Collaboration | 1Arizona State University, Tempe, USA 2Virginia Tech, Arlington, USA 3NEC Laboratories America, Princeton, USA |
| Pseudocode | Yes | Algorithm 1: RL-based data collection |
| Open Source Code | Yes | Code https://github.com/Nanxu Gong/ELLM-FT |
| Open Datasets | Yes | Data Descriptions. We collected 12 datasets from UCIrvine, Lib SVM, Kaggle, and Open ML. We evaluated our method and baseline methods on two major predictive tasks: 1) Classification (C); and 2) Regression (R). Table 1 shows the detailed statistics of the data sets. |
| Dataset Splits | Yes | We performed 5-fold stratified cross-validation to reduce random errors in experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details like CPU/GPU models, processor types, or memory amounts used for running the experiments. It only mentions using 'Llama-2-13B-chat-hf' as the backbone LLM. |
| Software Dependencies | No | The paper mentions 'Llama-2-13B-chat-hf' as the backbone LLM and 'Random Forest (RF)' as the downstream model, but it does not provide specific version numbers for any software libraries, programming languages, or other dependencies. |
| Experiment Setup | No | The paper describes evaluation metrics (F-1 score, 1-RAE) and cross-validation (5-fold stratified cross-validation), but it does not provide specific numerical hyperparameters for the LLM (e.g., prompt iterations, specific number of few-shot examples, M and T for Algorithm 1), the RL data collector, or the downstream Random Forest model's configuration. |