PAI at SemEval-2026 Task 3: An LLM and Data Redistribution Adaptation-Based Predictive Strategy for Valence-Arousal Scores

AbstractTo address the valence and arousal score prediction task in Dimensional Aspect-Based Sentiment Analysis (DimABSA), we propose a two-stage strategy. In the first stage, we conduct post-training on a Large Language Model (LLM) via a Supervised Fine-Tuning (SFT) scheme, followed by generating initial predictions for valence and arousal scores. In the second stage, we perform distribution adaptation on the initial results by leveraging the training set distribution through various techniques, including Gaussian distribution modeling, quantile mapping, and the Sinkhorn algorithm.