This study evaluates how Large Language Models (LLMs) perform deep legal reasoning on Taiwanese Status Law questions and investigates how Chain-of-Thought (CoT) prompting affects interpretability, alignment, and generalization. Using a two-stage evaluation framework, we first decomposed six real legal essay questions into 68 sub-questions covering issue spotting, statutory application, and inheritance computation. In Stage Two, full-length answers were collected under baseline and CoT-prompted conditions. Four LLMs—ChatGPT-4o, Gemini, Grok3, and Copilot—were tested. Results show CoT prompting significantly improved accuracy for Gemini (from 83.2% to 94.5%, p < 0.05) and Grok3, with moderate but consistent gains for ChatGPT and Copilot. Human evaluation of full-length responses revealed CoT answers received notably higher scores in issue coverage and reasoning clarity, with ChatGPT and Gemini gaining +2.67 and +1.92 points respectively. Despite these gains, legal misclassifications persist, highlighting alignment gaps between surface-level fluency and expert legal reasoning. This work opens the black box of legal NLU by tracing LLM reasoning chains, quantifying performance shifts under structured prompting, and providing a diagnostic benchmark for complex, open-ended legal tasks beyond multiple-choice settings.

This paper describes the NTULAW team’s participation in the ROCLING 2025 Dimensional Sentiment Analysis (DSA) shared task, which focuses on predicting valence and arousal ratings for Chinese doctors’ self-reflection texts. Unlike previous editions of the DSA task that targeted words, phrases, or educational comments, this year’s dataset consists of domain-specific multi-sentence medical narratives, posing challenges such as low-arousal writing styles, implicit emotion expressions, and discourse complexity. To address the domain shift between general affective resources (Chinese EmoBank) and medical reflections, we designed a multi-scale BERT-based architecture and explored different data selection strategies. Our final system adopted a hybrid submission: using a model trained solely on doctors’ annotations for arousal prediction, and a combined model with Chinese EmoBank for valence prediction. The system achieved stable performance, ranking third among six participating teams. Error analysis shows systematic overestimation of implicit or negated expressions for valence and regression toward mid-range predictions for arousal. We conclude with limitations of relying only on BERT and outline future work involving domain adaptation, discourse-aware modeling, and large language models (LLMs).