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..
Boosting Few-Shot Text Classification via Distribution Estimation
Authors: Han Liu, Feng Zhang, Xiaotong Zhang, Siyang Zhao, Fenglong Ma, Xiao-Ming Wu, Hongyang Chen, Hong Yu, Xianchao Zhang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on eight few-shot text classification datasets show that the proposed method outperforms state-of-the-art baselines significantly. |
| Researcher Affiliation | Academia | 1 Dalian University of Technology, Dalian, China 2 Peking University, Beijing, China 3 The Pennsylvania State University, Pennsylvania, USA 4 The Hong Kong Polytechnic University, Hong Kong, China 5 Zhejiang Lab, Hangzhou, China |
| Pseudocode | No | The paper describes algorithmic steps in prose and mathematical formulas but does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | We follow (Chen et al. 2022) to conduct experiments on eight text classification datasets, including four intent detection datasets: Banking77, HWU64, Clinic150, and Liu57, and four news or review classification datasets: Huff Post, Amazon, Reuters, and 20News. |
| Dataset Splits | Yes | All reported results are from 5 different runs, and in each run the training, validation and testing classes are randomly resampled. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'bert-base-uncased model' and 'Adam W' optimizer but does not specify version numbers for these or other critical software dependencies like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | We set R = 10 for the news or review classification task, while R = 4 for the intent detection task. For the loss function, we set λ = 0.1, and optimize the model parameters using Adam W (Loshchilov and Hutter 2019) with the initial learning rate 0.00001 and dropout rate 0.1. During distribution sampling, in 1-shot and 5-shot scenarios, we generate 20 and 100 samples per class respectively. |