Unpacking Legal Reasoning in LLM s: Chain-of-Thought as a Key to Human-Machine Alignment in Essay-Based NLU Tasks (2025)

Abstract: 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.

## introduction legal reasoning presents unique challenges for large language models (llms) due to the logicintensive and statute-bound nature of legal texts. existing evaluations often focus on multiple-choice formats that fail to capture the stepwise reasoning required in legal analysis. this study introduces a chain-of-thought (cot) prompting strategy tailored for legal essay questions. by guiding llms through decomposed sub-questions, we aim * corresponding author. orcid: 0000-0002-7101-5272 to reveal how structured prompting enhances interpretability, aligns with human reasoning, and supports complex legal inference. we propose a two-stage diagnostic evaluation to assess how cot affects legal reasoning generalization. stage one decomposes six real legal exam questions into 68 sub-questions evaluating fact recognition, statutory application, and logic chaining. stage two compares full-length responses under baseline and cot-prompted conditions. four llms are tested, and answers are scored by both a professor and a student, enabling analysis of human-machine agreement and misalignment across reasoning dimensions. by moving beyond answer correctness toward an analysis of legal reasoning structure, this study contributes new methods for evaluating alignment, generalization, and interpretability in legal nlu tasks. it offers a scalable benchmark and experimental protocol to guide the development of more transparent and human-aligned legal language systems. ## related work with the advancement of large language models (llms), their applications in the legal domain have grown rapidly, yielding promising results in tasks such as contract analysis, judgment summarization, legal consultation, and case prediction . to promote research in legal language processing, several benchmark datasets and evaluation platforms for llms have emerged in recent years, including lexglue @xcite , legalbench @xcite , and the col-iee competition on statutory entailment and retrieval. these benchmarks primarily cover tasks such as multiple-choice questions, case classification, statute matching, and legal question answering. however, most of them focus on englishlanguage corpora and closed-form problems, lack-ing the design needed to evaluate the type of openended, reasoning-intensive essay questions encountered in real-world legal practice. as noted by the creators of legalbench, current benchmarks "still fall short of comprehensively evaluating the open-ended reasoning required in law school exams and legal writing assignments" @xcite , which often involve deep statutory subsumption and integrated legal analysis. chain-of-thought (cot) prompting has recently emerged as a promising strategy for improving multi-step reasoning and computation, validated on tasks such as math word problems and commonsense reasoning @xcite . cot prompting has to be effective even without in-context examples: simple natural language cues like "let's think step by step" can activate internal reasoning chains and significantly improve performance in zero-shot settings @xcite . cot has since been widely applied in mathematical reasoning (e.g., gsm8k, math), logic puzzles, scientific domains, and programming tasks. prior studies consistently find cot especially useful for tasks that require intermediate inference steps, as it helps maintain contextual coherence and supports longer, structured chains of reasoning. although legal reasoning itself is inherently a multi-step logical task, systematic analysis of cot prompting in the legal domain remains limited. some notable attempts include the kis team's interpretable cot strategy in the coliee 2024 entailment task, which enhances interpretability in statutory subsumption through structured prompting @xcite . another example is the legalgpt framework, which integrates cot modules within a multi-agent architecture to simulate the collaborative logic of real-world legal practice @xcite . mainstream approaches to evaluating legal llms typically rely on automated metrics (e.g., accuracy, bleu, f1-score) or multiple-choice style datasets to compare model performance. however, such closed-form evaluations fail to reflect the logical depth and reasoning quality required for open-ended generative tasks. recent studies have begun to incorporate human evaluation to better assess consistency and subsumption performance in long-form legal qa. representative systems such as length-controlled alpacaeval @xcite we conduct qualitative analysis by selecting cases with high inter-rater agreement to compare the logical structure of reasoning chains, thereby supplementing the current lack of process transparency and error traceability in legal llm evaluation.