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..
ADEPT: A DEbiasing PrompT Framework
Authors: Ke Yang, Charles Yu, Yi R. Fung, Manling Li, Heng Ji
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate ADEPT on several widely acknowledged debiasing benchmarks and downstream tasks, and find that it achieves competitive results while maintaining (and in some cases even improving) the PLM s representation ability. |
| Researcher Affiliation | Academia | Ke Yang1, Charles Yu2, Yi R. Fung2, Manling Li2, Heng Ji2 1Tsinghua University 2University of Illinois Urbana-Champaign |
| Pseudocode | Yes | Algorithm 1: ADEPT: a debiasing algorithm for contextualized word embeddings. |
| Open Source Code | Yes | 1The code and data are publicly available at https://github.com/ Empath Yang/ADEPT. |
| Open Datasets | Yes | For the sentences associated with the word tuples, we draw sentences from News-Commentary v15 (Tiedemann 2012) for the gender setting and sentences from Book Corpus (Zhu et al. 2015) and News-Commentary v15 (Tiedemann 2012) for the relgions setting. Since the original Book Corpus is no longer available, we use (lewtun et al. 2022) which is an open source replica. |
| Dataset Splits | No | The paper explicitly mentions test examples for evaluation but does not provide specific details on a validation dataset split used during the training or hyperparameter tuning of ADEPT itself. |
| Hardware Specification | Yes | All the experiments are conducted on two Ge Force RTX 3090 GPUs and in a Linux operating system. |
| Software Dependencies | No | The paper mentions using BERT-LARGE-UNCASED from Hugging Face and the Adam optimizer but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We set λ in Equation 1 to be 7 and ρ in Equation 4 to be 15. We use Adam (Kingma and Ba 2014) to optimize the objective function. During the debiasing process, our learning rate is 5e-5 and our batchsize is 32. |