Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Better Fine-Tuning via Instance Weighting for Text Classification
Authors: Zhi Wang, Wei Bi, Yan Wang, Xiaojiang Liu7241-7248
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that IW-Fit can consistently improve the classification accuracy on the target domain. |
| Researcher Affiliation | Collaboration | 1Department of Control and Systems Engineering, Nanjing University, Nanjing, China 2Tencent AI Lab, Shenzhen, China |
| Pseudocode | Yes | Algorithm 1: Instance Weighting based Fine-tuning (IW-Fit) for text classification |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The source dataset from 2015 Amazon reviews (Zhang, Zhao, and Le Cun, 2015; Conneau et al., 2016) is mainly about products of online shopping. The target dataset of Yelp reviews is obtained from the latest 2018 Yelp Dataset Challenge |
| Dataset Splits | No | The paper specifies 10,000 training instances and 1,000 testing instances for the target dataset but does not explicitly mention a separate validation split or its size. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions software components like LSTM and CNN but does not provide specific version numbers for any software libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | The hyperparameters are set as: vocabulary size = 30, 000, embedding size = 100, LSTM hidden size = 200, FC layer size = 200, dropout rate = 0.2, L2 regularization = 1e 4, batch size = 32, fine-tuning epochs = 50, burn-in epochs = 10, learning rate = 1e 3. |