@article{Xue2025, 
author = {Wei Xue and Han Jiang and Junwen Duan and Zhe Qu and Jianxin Wang},
title = {You Only Forward Once: Prediction and Rationalization in a Single Forward Pass},
year = {2025},
journal = {Big Data Mining and Analytics},
keywords = {natural language processing, unsupervised rationale extraction, Pre-trained Language Model (PLM), explaining AI model},
url = {https://www.sciopen.com/article/10.26599/BDMA.2025.9020084},
doi = {10.26599/BDMA.2025.9020084},
abstract = {Unsupervised rationale extraction aims to identify concise text snippets supporting model predictions without annotated rationales. However, existing two-phase approaches often suffer from interlocking (where predictors overfit to poorly generated rationales) and spurious correlations (where models capture non-causal relationships between text and labels). To address these challenges, we propose a novel single-phase framework called You Only Forward Once (YOFO) that simultaneously explains and predicts. YOFO utilizes a Pre-trained Language Model (PLM) to gradually remove unimportant tokens, with the remaining tokens considered as rationales. We introduce a length configuration list to flexibly control the proportion of remaining tokens in each layer. Importantly, we replace the traditional zero-out operation for token deletion with Attention Mask Deletion (AMD), which significantly improves model performance. Experiments on the BeerAdvocate and Hotel Review datasets demonstrate that YOFO achieves state-of-the-art rationalization performance, improving token-level F1 by up to 14.5% compared to previous methods. Our approach effectively mitigates the interlocking and spurious correlation issues prevalent in two-phase models by eliminating the need for separate generation and prediction phases, offering a relatively robust and efficient approach for explaining Artificial Intelligence (AI) model predictions.}
}