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..
Personalized LoRA for Human-Centered Text Understanding
Authors: You Zhang, Jin Wang, Liang-Chih Yu, Dan Xu, Xuejie Zhang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on four benchmark datasets show that the proposed method outperforms existing methods in full/few/zero-shot learning scenarios for the HCTU task, even though it has fewer trainable parameters. |
| Researcher Affiliation | Academia | You Zhang1, Jin Wang1*, Liang-Chih Yu2*, Dan Xu1, Xuejie Zhang1 1School of Information Science and Engineering, Yunnan University, Yunnan, P.R.China 2Department of Information Management, Yuan Ze University, Taiwan |
| Pseudocode | No | The paper describes its methods and formulations using natural language and mathematical equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | For reproducibility, the code for this paper is available at: https://github.com/yoyo-yun/PLo RA. |
| Open Datasets | Yes | The used datasets include IMDB, YELP, GDRD, and PPR, where a collection of data is associated with different users. To simulate complex and practical situations in real-world applications, all datasets are individually divided into two parts DA and DB where DA contains much larger samples than DB and DB aims to simulate cold-start scenarios, as formulated in Section Methodology. |
| Dataset Splits | Yes | Either DA or DB, it splits into train, dev, and test data for experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using pre-trained language models like BERT, RoBERTa, and Flan-T5, but it does not specify any software dependencies with version numbers (e.g., PyTorch version, specific library versions). |
| Experiment Setup | Yes | For the reproduction of experiments, more implementation details of hyperparameters were reported in Appendices. |