Optimize Incompatible Parameters Through Compatibility-aware Knowledge Integration
Authors: Zheqi Lv, Keming Ye, Zishu Wei, Qi Tian, Shengyu Zhang, Wenqiao Zhang, Wenjie Wang, Kun Kuang, Tat-Seng Chua, Fei Wu
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various recommendation and language datasets show that CKI can effectively optimize incompatible parameters under multiple tasks and settings to break through the training limit of the original model without increasing the inference cost. |
| Researcher Affiliation | Collaboration | 1Zhejiang University, Hangzhou, China 2Tencent TEG., Shenzhen, China 3 National University of Singapore, Singapore EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper includes a methodological section with descriptions and a diagram (Figure 2) outlining the CKI process, but it does not present any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, a link to a code repository, or information that the code is provided in supplementary materials. |
| Open Datasets | Yes | In recommendation tasks, we evaluated our method on 4 widely used datasets Amazon-Beauty (Beauty), Douban-Book (Book), Douban-Music (Music), Movielens-1M (Movielens). In language tasks, we evaluated our method on 2 widely used datasets RTE, and SST-2. |
| Dataset Splits | No | The paper mentions the datasets used but does not specify the exact training, validation, or test splits (e.g., percentages, sample counts, or references to predefined splits with citations) needed for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to conduct the experiments, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper mentions various models and methods but does not specify the software dependencies (e.g., programming languages, libraries, frameworks) along with their version numbers that were used for implementation or experimentation. |
| Experiment Setup | No | The paper discusses the experimental results and states that the integrated model needs "only one epoch of retraining to achieve better performance" for fine-tuning. However, it does not provide specific hyperparameters like learning rates, batch sizes, optimizers, or other detailed training configurations for the initial model training or fine-tuning. |