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<body class="c65 doc-content">
<h2 class="c36"><span class="c3-big">Evaluating Generalist LLMs on Legal Knowledge Tasks: A Comprehensive
Benchmark Integrating Bloom’s Taxonomy on IT Regulations</span></h2>
<p class="c2"><span class="c3"></span></p>
<p class="c18 c72"><span class="c3">Abstract: </span><span class="c1">This paper introduces a comprehensive
benchmark designed to rigorously evaluate general-purpose Large Language Models (LLMs) within legal
contexts, with a particular emphasis on regulatory compliance in Information Technology (IT). By employing
Bloom’s taxonomy as a structural framework, we systematically organize evaluation tasks across
cognitive dimensions—memorization, understanding, analyzing, applying, evaluating, and creating. This
benchmark features a meticulously constructed dataset containing multiple- choice questions (MCQs) and
innovative open-ended analytical tasks tailored for legal analysis. Furthermore, we outline the creation of
an associated training dataset using this structured approach to improve the practical performance of LLMs,
initially focusing on IT regulations and subsequently extending to broader regulatory domains.</span></p>
<p class="c2"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-0 start" start="1">
<li class="c24 li-bullet-0"><span class="c3">Introduction: </span><span class="c1">Effective legal reasoning and
regulatory compliance are essential yet cognitively demanding areas where precision and comprehensive
understanding are paramount. Existing benchmarks, such as LEXam (2025), LegalBench (2023), LLeQA (2023),
and LexEval (2024), have contributed significantly to assessing legal reasoning capabilities but
typically lack detailed cognitive structuring or domain-specific regulatory contexts. Historically,
Bloom’s taxonomy has been instrumental in structuring cognitive assessments, providing a
systematic framework to evaluate a range of cognitive tasks essential for nuanced legal reasoning.
Extending this cognitive framework explicitly into IT regulatory scenarios addresses existing gaps by
providing a detailed and structured assessment method specifically tailored for evaluating generalist
LLM capabilities.</span></li>
</ol>
<p class="c9"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-0" start="2">
<li class="c24 li-bullet-0"><span class="c3">Related Work: </span><span class="c1">Previous influential
benchmarks include:</span></li>
</ol>
<p class="c2"><span class="c1"></span></p>
<ul class="c5 lst-kix_list_4-0 start">
<li class="c0 c55 li-bullet-1"><span class="c3">LEXam (2025)</span><span class="c1">: Focused on traditional
legal examination formats emphasizing cognitive recall and comprehension through MCQs and open-ended
questions.</span></li>
<li class="c0 c64 li-bullet-1"><span class="c3">LegalBench (2023)</span><span class="c1">: Provided in-depth
evaluations through varied legal analytical and practical application scenarios.</span></li>
<li class="c0 c46 li-bullet-1"><span class="c3">LLeQA (2023)</span><span class="c1">: Specialized in assessing
textual coherence and detailed interpretative reasoning.</span></li>
<li class="c0 c32 li-bullet-1"><span class="c3">LexEval (2024)</span><span class="c1">: Evaluated analytical
proficiency extensively within Chinese legal contexts.</span></li>
</ul>
<p class="c9"><span class="c1"></span></p>
<p class="c18 c76"><span class="c1">Our benchmark synthesizes these approaches and introduces specialized IT
regulatory tasks, effectively combining cognitive rigor with practical regulatory relevance.</span></p>
<p class="c2"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-0" start="3">
<li class="c24 li-bullet-0">
<h2 style="display:inline"><span class="c3">Dataset Construction:</span></h2>
</li>
</ol>
<p class="c2"><span class="c3"></span></p>
<p class="c18"><span class="c1">Our dataset integrates Bloom’s taxonomy, meticulously structured to evaluate
various cognitive abilities within legal contexts, especially focusing on Information Technology (IT)
regulations. The dataset comprises three principal question types designed to thoroughly evaluate LLM
capabilities:</span></p>
<p class="c9"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-1 start" start="1">
<li class="c26 li-bullet-2">
<h1 style="display:inline"><span class="c6">Multiple-Choice Questions (MCQs):</span></h1>
</li>
</ol>
<p class="c36"><span class="c1">MCQs are carefully crafted to assess foundational cognitive skills such as
memorization and basic comprehension of legal texts and regulatory standards. Questions explicitly target
recall accuracy, terminology clarity, and immediate interpretive abilities.</span></p>
<p class="c7"><span class="c1"></span></p>
<ul class="c5 lst-kix_list_5-2 start">
<li class="c0 c62 li-bullet-1"><span class="c3">Principle: </span><span class="c1">MCQs assess precise recall
and basic understanding of legal provisions and regulatory terms.</span></li>
<li class="c0 li-bullet-1"><span class="c3">Metrics: </span><span class="c1">Accuracy rate, response
consistency, and recall precision.</span></li>
</ul>
<p class="c2"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-1" start="2">
<li class="c11 li-bullet-3">
<h1 style="display:inline"><span class="c6">General Open-Ended Questions:</span></h1>
</li>
</ol>
<p class="c15 c69"><span class="c1">These tasks require LLMs to demonstrate intermediate cognitive skills, including
interpretation, analysis, and application of broader legal and regulatory principles. General open-ended
questions typically present scenarios without sector-specific constraints, assessing the model’s
ability to reason and articulate legal arguments clearly and accurately.</span></p>
<p class="c9"><span class="c1"></span></p>
<ul class="c5 lst-kix_list_5-2">
<li class="c23 li-bullet-1"><span class="c3">Principle: </span><span class="c1">Assess analytical depth,
interpretative coherence, and application accuracy.</span></li>
<li class="c0 c52 li-bullet-1"><span class="c3">Metrics: </span><span class="c1">Precision of legal reasoning,
interpretative coherence (BLEU and ROUGE scores), and qualitative evaluation by legal experts.</span>
</li>
</ul>
<p class="c2"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-1" start="3">
<li class="c11 li-bullet-4">
<h1 style="display:inline"><span class="c6">Domain-Specific Analytical Questions:</span></h1>
</li>
</ol>
<p class="c22"><span class="c1">These tasks represent the core of our innovative dataset, structured to rigorously
evaluate advanced cognitive skills such as deep analysis, critical evaluation, and creative synthesis within
specific regulatory contexts (initially IT regulation). Each task explicitly aligns with Bloom's
taxonomy:</span></p>
<p class="c7"><span class="c1"></span></p>
<ul class="c5 lst-kix_list_5-2">
<li class="c0 c43 li-bullet-1"><span class="c3">Analyzing: </span><span class="c1">Dissect complex regulatory
scenarios, identify critical legal issues, compliance requirements, and their implications.</span></li>
<li class="c0 c44 li-bullet-1"><span class="c3">Applying: </span><span class="c1">Direct application of legal
frameworks to practical scenarios, ensuring regulatory adherence.</span></li>
<li class="c0 c4 li-bullet-1"><span class="c3">Evaluating: </span><span class="c1">Critical assessment tasks
involving analysis of legal jurisdictions, risk assessments, and forecasting legal outcomes.</span></li>
<li class="c0 c16 li-bullet-1"><span class="c3">Creating: </span><span class="c1">Generative tasks requiring the
development of innovative compliance strategies and detailed policy recommendations.</span></li>
</ul>
<p class="c9"><span class="c1"></span></p>
<p class="c18"><span class="c1">Example Task:</span></p>
<p class="c2"><span class="c1"></span></p>
<ul class="c5 lst-kix_list_5-2">
<li class="c0 c41 li-bullet-1"><span class="c3">Context: </span><span class="c1">AI CNIL v. Doctissimo (2023):
CNIL sanctions Doctissimo for breaches of GDPR through online symptom checker algorithms.</span></li>
<li class="c23 c66 li-bullet-1"><span class="c3">Facts: </span><span class="c1">Doctissimo operated an online
symptom checker processing users' health data without explicit consent, breaching data minimization
principles, and retaining sensitive data excessively.</span></li>
<li class="c0 li-bullet-1"><span class="c3">Legal Rules: </span><span class="c1">Articles 6, 9, and 5 GDPR; EU
AI Act Recital 38 and Article 10.</span></li>
<li class="c0 c75 li-bullet-1"><span class="c3">Expected Conditions: </span><span class="c1">Explicit consent;
appropriate data retention; transparency in automated decision-making.</span></li>
<li class="c0 c42 li-bullet-1"><span class="c3">Application Explanation: </span><span class="c1">CNIL identified
violations due to the lack of explicit consent, insufficient user information, and excessive data
retention, highlighting high- risk implications under AI regulatory standards.</span></li>
<li class="c0 c21 li-bullet-1"><span class="c3">Expected Result: </span><span class="c1">A fine and mandated
corrective measures for compliance improvement.</span></li>
<li class="c47 li-bullet-5"><span class="c3">Case Reference: </span><span class="c1">CNIL Sanction Decision No.
SAN-2023-001, Doctissimo, January 2023.</span></li>
<li class="c23 c83 li-bullet-1"><span class="c3">Counterfactual Scenario: </span><span class="c1">Implementation
of anonymized data processing methods and metadata collection only.</span></li>
<li class="c0 c29 li-bullet-5"><span class="c3">New Fact: </span><span class="c1">Redesigned system
incorporating anonymized user behavior logging without identifiable data.</span></li>
<li class="c0 c31 li-bullet-1"><span class="c3">Expected Outcome: </span><span class="c1">Compliance with GDPR
and lower risk categorization under the AI Act.</span></li>
<li class="c0 li-bullet-1"><span class="c3">Arguments Pro: </span><span class="c1">Promotes digital health
innovation, rapid diagnostic access for users.</span></li>
<li class="c0 li-bullet-5"><span class="c3">Arguments Contra: </span><span class="c1">Privacy violations,
consent breaches, and lack of transparency.</span></li>
<li class="c0 c84 li-bullet-1"><span class="c3">Paraphrases: </span><span class="c1">Automated health data
processing requires explicit consent, transparency, and strict adherence to GDPR and AI regulatory
frameworks.</span></li>
</ul>
<p class="c7"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-1" start="4">
<li class="c11 li-bullet-4">
<h1 style="display:inline"><span class="c6">Scoring Methodology and Metrics:</span></h1>
</li>
</ol>
<p class="c22"><span class="c1">Each task type employs specific scoring methodologies and metrics:</span></p>
<p class="c2"><span class="c1"></span></p>
<ul class="c5 lst-kix_list_5-2">
<li class="c0 li-bullet-5"><span class="c3">MCQs: </span><span class="c1">Objective accuracy and recall.</span>
</li>
<li class="c0 c27 li-bullet-1"><span class="c3">General Open-Ended Questions: </span><span
class="c1">Qualitative expert reviews, coherence metrics, and interpretative accuracy.</span></li>
<li class="c0 c80 li-bullet-5"><span class="c3">Domain-Specific Analytical Questions: </span><span
class="c1">Multi-dimensional assessments combining legal accuracy, interpretive depth, cognitive rigor,
and practical applicability scores from experts.</span></li>
</ul>
<p class="c9"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-0" start="4">
<li class="c24 li-bullet-0">
<h2 style="display:inline"><span class="c3">Experimental Setup:</span></h2>
</li>
</ol>
<p class="c2"><span class="c3"></span></p>
<p class="c18"><span class="c1">Our experimental setup adopts a rigorous evaluation framework that combines
customized metrics aligned explicitly with task types and cognitive levels defined by Bloom’s
taxonomy. Additionally, we incorporate metrics specific to the inherent nature of AI computational tasks,
namely Generation, Extraction, Single-Label Classification (SLC), and Multi-Label Classification
(MLC).</span></p>
<p class="c7"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-1" start="5">
<li class="c11 li-bullet-3">
<h1 style="display:inline"><span class="c6">Customized Metrics by Task Type and Cognitive Level:</span></h1>
</li>
</ol>
<p class="c22"><span class="c1">To effectively evaluate the LLM performance across diverse legal cognitive tasks, we
introduce metrics tailored specifically to each cognitive dimension and task format:</span></p>
<p class="c9"><span class="c1"></span></p>
<ul class="c5 lst-kix_list_5-2">
<li class="c0 li-bullet-1">
<h2 style="display:inline"><span class="c3">Memorization (MCQs):</span></h2>
</li>
</ul>
<ul class="c5 lst-kix_list_5-3 start">
<li class="c39 li-bullet-6"><span class="c3">Accuracy: </span><span class="c1">Percentage of correctly answered
MCQs.</span></li>
<li class="c78 li-bullet-1"><span class="c3">Precision and Recall: </span><span class="c1">Assessed for
questions involving terminological and definitional clarity.</span></li>
</ul>
<ul class="c5 lst-kix_list_5-2">
<li class="c23 li-bullet-1">
<h2 style="display:inline"><span class="c3">Understanding (General Open-Ended Questions):</span></h2>
</li>
</ul>
<ul class="c5 lst-kix_list_5-3 start">
<li class="c75 c82 li-bullet-1"><span class="c3">BLEU and ROUGE Scores: </span><span class="c1">Evaluate the
coherence and fluency of textual responses.</span></li>
<li class="c10 li-bullet-1"><span class="c3">Legal Concept Accuracy: </span><span class="c1">Expert-validated
accuracy in interpreting and expressing legal principles.</span></li>
</ul>
<ul class="c5 lst-kix_list_5-2">
<li class="c23 li-bullet-5">
<h2 style="display:inline"><span class="c3">Analyzing and Applying (Domain-Specific Analytical
Tasks):</span></h2>
</li>
</ul>
<ul class="c5 lst-kix_list_5-3 start">
<li class="c73 li-bullet-1"><span class="c3">Analytical Depth Score: </span><span class="c1">Expert-graded scale
assessing depth of legal analysis.</span></li>
<li class="c45 li-bullet-5"><span class="c3">Compliance Application Accuracy: </span><span class="c1">Percentage
accuracy based on correctly identified and applied regulatory criteria.</span></li>
</ul>
<ul class="c5 lst-kix_list_5-2">
<li class="c23 li-bullet-6">
<h2 style="display:inline"><span class="c3">Evaluating and Creating (Advanced Domain-Specific Tasks):</span>
</h2>
</li>
</ul>
<ul class="c5 lst-kix_list_5-3 start">
<li class="c39 li-bullet-6"><span class="c3">Risk Assessment Accuracy: </span><span class="c1">Evaluates the
model’s ability to correctly identify</span></li>
</ul>
<p class="c33"><span class="c1">and assess compliance risks.</span></p>
<ul class="c5 lst-kix_list_5-3">
<li class="c20 li-bullet-1"><span class="c3">Creativity and Practicality Score: </span><span class="c1">Expert
qualitative assessment rating innovative legal solutions and strategies.</span></li>
</ul>
<p class="c7"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-1" start="6">
<li class="c11 li-bullet-4">
<h1 style="display:inline"><span class="c6">Metrics Derived by AI Task Nature:</span></h1>
</li>
</ol>
<p class="c32 c70"><span class="c1">To further refine evaluations, we incorporate metrics categorized according to
the intrinsic nature of the AI computations involved in each task:</span></p>
<p class="c2"><span class="c1"></span></p>
<ul class="c5 lst-kix_list_5-2">
<li class="c0 li-bullet-1">
<h2 style="display:inline"><span class="c3">Generation Tasks (e.g., Creating):</span></h2>
</li>
</ul>
<ul class="c5 lst-kix_list_5-3 start">
<li class="c25 li-bullet-5"><span class="c3">Coherence and Fluency (BLEU, ROUGE): </span><span
class="c1">Measures readability and semantic coherence.</span></li>
<li class="c54 li-bullet-1"><span class="c3">Novelty Index: </span><span class="c1">Expert-assessed originality
and innovation in generated responses.</span></li>
</ul>
<ul class="c5 lst-kix_list_5-2">
<li class="c23 li-bullet-1">
<h2 style="display:inline"><span class="c3">Extraction Tasks (e.g., Understanding, Analyzing):</span></h2>
</li>
</ul>
<ul class="c5 lst-kix_list_5-3 start">
<li class="c30 li-bullet-5"><span class="c3">Extraction Precision and Recall: </span><span
class="c1">Quantitative metrics assessing the model's capability to accurately identify and extract
relevant legal facts, principles, and issues from textual scenarios.</span></li>
</ul>
<ul class="c5 lst-kix_list_5-2">
<li class="c0 c35 li-bullet-1">
<h2 style="display:inline"><span class="c3">Single-Label Classification (SLC) Tasks (MCQs, simple regulatory
compliance checks):</span></h2>
</li>
</ul>
<ul class="c5 lst-kix_list_5-3 start">
<li class="c39 li-bullet-1"><span class="c3">Accuracy Rate: </span><span class="c1">Proportion of correct
single-classification responses.</span></li>
<li class="c79 li-bullet-1"><span class="c3">Confidence Score Distribution: </span><span class="c1">Statistical
analysis of model confidence levels for correct vs. incorrect classifications.</span></li>
</ul>
<ul class="c5 lst-kix_list_5-2">
<li class="c17 li-bullet-5">
<h2 style="display:inline"><span class="c3">Multi-Label Classification (MLC) Tasks (complex regulatory
assessments, compliance verification tasks):</span></h2>
</li>
</ul>
<ul class="c5 lst-kix_list_5-3 start">
<li class="c51 li-bullet-5"><span class="c3">Multi-label Accuracy (Subset Accuracy): </span><span
class="c1">Evaluates the precision in identifying multiple applicable legal criteria
simultaneously.</span></li>
<li class="c12 li-bullet-5"><span class="c3">F1-Score (Macro and Micro): </span><span class="c1">Assesses
balanced precision and recall across multiple regulatory criteria.</span></li>
</ul>
<p class="c2"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-1" start="7">
<li class="c11 li-bullet-2">
<h1 style="display:inline"><span class="c6">Scoring and Expert Review Methodology:</span></h1>
</li>
</ol>
<p class="c68"><span class="c1">All quantitative metrics are complemented by qualitative expert evaluations. Legal
experts systematically review a representative sample of generated outputs, scoring them based on accuracy,
interpretive validity, regulatory relevance, and practical applicability.</span></p>
<p class="c7"><span class="c1"></span></p>
<ul class="c5 lst-kix_list_5-2">
<li class="c0 c60 li-bullet-1"><span class="c3">Expert Validity Rating: </span><span class="c1">Scale of
1–5 assessing overall legal correctness and applicability.</span></li>
<li class="c0 c28 li-bullet-1"><span class="c3">Regulatory Compliance Score: </span><span
class="c1">Expert-rated accuracy of models in identifying, evaluating, and applying regulatory criteria
correctly.</span></li>
</ul>
<p class="c9"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-1" start="8">
<li class="c50 li-bullet-7">
<h1 style="display:inline"><span class="c6">Tooling and Technical Considerations:</span></h1>
</li>
</ol>
<p class="c22"><span class="c1">In addition to benchmark-level assessments, our SaaS platform “Jessica”
by Contractzlab was</span></p>
<p class="c18"><span class="c1">employed to support human annotation and evaluation. Jessica integrates a
compliance</span></p>
<p class="c36"><span class="c1">scoring engine tailored to regulatory assessment logic. This system enables precise
scoring of model responses against predefined rules and compliance thresholds, including:</span></p>
<p class="c7"><span class="c1"></span></p>
<ul class="c5 lst-kix_list_5-2">
<li class="c0 li-bullet-1"><span class="c1">Legal principle matching.</span></li>
<li class="c0 li-bullet-5"><span class="c1">Alignment with sector-specific obligations.</span></li>
<li class="c0 li-bullet-1"><span class="c1">Scoring based on automated legal heuristics.</span></li>
</ul>
<p class="c2"><span class="c1"></span></p>
<p class="c14"><span class="c61">Computational Efficiency: </span><span class="c1">Average inference time per
question type/task nature.</span></p>
<p class="c9"><span class="c1"></span></p>
<p class="c18"><span class="c61">Scalability and Generalizability: </span><span class="c1">Assessed through task
performance consistency across multiple regulatory domains and contexts.</span></p>
<p class="c2"><span class="c1"></span></p>
<p class="c18"><span class="c1">This comprehensive experimental setup, integrating tailored cognitive and
computational metrics, ensures a nuanced, robust, and practical evaluation of generalist LLM performance in
legal and regulatory tasks, providing clear guidance for model improvements and practical
deployments.</span></p>
<p class="c2"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-0" start="5">
<li class="c24 li-bullet-0">
<h2 style="display:inline"><span class="c3">Results and Analysis:</span></h2>
</li>
</ol>
<p class="c9"><span class="c3"></span></p>
<p class="c18"><span class="c1">We conducted an exhaustive evaluation of multiple general-purpose Large Language
Models (LLMs), including DeepSeek, Llama-4-Maverick, Mistral, Phi-4, GPT-4.1, and GPT-4o, across a diverse
set of legal tasks categorized by Bloom’s taxonomy. The results illustrate substantial variability in
performance, underscoring strengths and limitations inherent to each model.</span></p>
<p class="c49"><span class="c8"></span></p>
<p class="c58"><span
style="overflow: hidden; display: inline-block; margin: 0.00px 0.00px; border: 0.00px solid #000000; transform: rotate(0.00rad) translateZ(0px); -webkit-transform: rotate(0.00rad) translateZ(0px); width: 709.49px; height: 436.60px;"><img
alt="" src="images/image1.jpg"
style="width: 709.49px; height: 436.60px; margin-left: 0.00px; margin-top: 0.00px; transform: rotate(0.00rad) translateZ(0px); -webkit-transform: rotate(0.00rad) translateZ(0px);"
title=""></span></p>
<p class="c53"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-1" start="9">
<li class="c11 li-bullet-2">
<h1 style="display:inline"><span class="c6">Quantitative Performance Overview</span></h1>
</li>
</ol>
<p class="c22"><span class="c1">Models exhibited distinct performance patterns:</span></p>
<ul class="c5 lst-kix_list_5-2">
<li class="c37 li-bullet-6"><span class="c3">Argument Mining (Accuracy)</span><span class="c1">: Mistral (96.9%)
and Phi-4 (66.4%) significantly outperformed other models. Conversely, GPT-4.1 (1.3%) exhibited poor
performance, indicating limitations in its capability to effectively extract nuanced arguments.</span>
</li>
<li class="c23 c56 li-bullet-5"><span class="c3">Article Recitation (Rouge-L)</span><span class="c1">: GPT-4.1
(45.9%) demonstrated superior capability in generating coherent textual content. GPT-4o and
Llama-4-Maverick also showed commendable performances, whereas DeepSeek and Phi-4 displayed limitations,
suggesting challenges with complex textual synthesis.</span></li>
<li class="c0 c63 li-bullet-1"><span class="c3">Consultation (Rouge-L)</span><span class="c1">: Llama-4-Maverick
(26.1%) and GPT-4o (24.5%) achieved relatively higher coherence, though overall performance across
models remained limited, pointing toward inherent complexities and contextual nuances involved in
consultative legal tasks.</span></li>
</ul>
<p class="c7"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-1" start="10">
<li class="c11 li-bullet-4">
<h1 style="display:inline"><span class="c6">Cognitive Level-Specific Insights</span></h1>
</li>
</ol>
<ul class="c5 lst-kix_list_5-2 start">
<li class="c74 li-bullet-1"><span class="c3">Memorization & Understanding</span><span class="c1">: GPT
variants excelled in foundational cognitive tasks requiring recall and basic comprehension, reflecting
robust pre-training on extensive factual datasets.</span></li>
<li class="c19 li-bullet-1"><span class="c3">Analyzing & Applying</span><span class="c1">: Mistral and Phi-4
excelled in complex analytical scenarios, demonstrating advanced capabilities in dissecting regulatory
contexts and applying specific compliance frameworks accurately.</span></li>
<li class="c0 c57 li-bullet-1"><span class="c3">Evaluating & Creating</span><span class="c1">: GPT-4o and
GPT-4.1 displayed notable strengths in advanced evaluative and generative tasks, adeptly crafting
contextually relevant legal arguments and strategic recommendations, albeit sometimes lacking detailed
specificity required by highly specialized regulatory contexts.</span></li>
</ul>
<p class="c2"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-1" start="11">
<li class="c11 li-bullet-3">
<h1 style="display:inline"><span class="c6">Detailed Comparative Analysis</span></h1>
</li>
</ol>
<p class="c15"><span class="c1">Quantitative analyses revealed nuanced insights into model performance:</span></p>
<p class="c9"><span class="c1"></span></p>
<ul class="c5 lst-kix_list_5-2">
<li class="c0 c71 li-bullet-5"><span class="c3">Generation Tasks</span><span class="c1">: GPT-4o demonstrated
consistent superiority in generative capabilities, showcasing both fluency and contextual coherence, as
evidenced by Rouge-L scores.</span></li>
<li class="c0 c40 li-bullet-6"><span class="c3">Extraction and Classification Tasks</span><span class="c1">:
Models like Mistral and Phi-4 excelled, particularly in tasks requiring precise extraction and accurate
classification of regulatory details, suggesting their suitability for structured analytical legal
applications.</span></li>
</ul>
<p class="c7"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-1" start="12">
<li class="c11 li-bullet-2">
<h1 style="display:inline"><span class="c6">Model Limitations and Performance Gaps</span></h1>
</li>
</ol>
<p class="c15"><span class="c1">Despite strengths, substantial limitations emerged across models:</span></p>
<p class="c2"><span class="c1"></span></p>
<ul class="c5 lst-kix_list_5-2">
<li class="c23 c48 li-bullet-1"><span class="c3">Generalist vs. Domain-Specific Contexts</span><span
class="c1">: All models, especially GPT-4.1, showed reduced efficacy in domain-specific tasks,
reflecting the intrinsic limitations of generalist models in highly specialized regulatory
scenarios.</span></li>
<li class="c0 c59 li-bullet-5"><span class="c3">Complex Legal Reasoning</span><span class="c1">: None of the
models consistently achieved high performance across all cognitive levels, indicating the need for more
specialized training and refinement to effectively handle nuanced legal analyses and regulatory
compliance tasks.</span></li>
</ul>
<p class="c9"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-0" start="6">
<li class="c34 li-bullet-8">
<h1 style="display:inline"><span class="c6">Expert Validation and Qualitative Assessment:</span></h1>
</li>
</ol>
<p class="c36"><span class="c1">Legal expert assessments validated quantitative findings, providing deeper
insights:</span></p>
<p class="c2"><span class="c1"></span></p>
<ul class="c5 lst-kix_list_3-0 start">
<li class="c13 li-bullet-1"><span class="c1">GPT variants received recognition for general clarity and logical
coherence but faced criticism for superficial handling of domain-specific regulatory nuances.</span>
</li>
<li class="c0 c81 li-bullet-1"><span class="c1">Mistral and Phi-4 garnered positive feedback for analytical
rigor and precision, yet experts noted occasional difficulties in contextual interpretation and
higher-order reasoning.</span></li>
</ul>
<p class="c2"><span class="c1"></span></p>
<p class="c14 c31"><span class="c1">Expert evaluations also highlighted the practical implications of model
performance limitations, emphasizing the importance of context-specific accuracy, interpretative depth, and
compliance relevance.</span></p>
<p class="c9"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-0" start="7">
<li class="c34 li-bullet-9">
<h1 style="display:inline"><span class="c6">Discussion and Future Directions:</span></h1>
</li>
</ol>
<p class="c15"><span class="c1">The detailed analyses underscore clear pathways for further research and model
improvement:</span></p>
<p class="c2"><span class="c1"></span></p>
<ul class="c5 lst-kix_list_2-0 start">
<li class="c0 c85 li-bullet-1"><span class="c3">Domain-Specific Fine-Tuning</span><span class="c1">: Targeted
fine-tuning on specific regulatory datasets is crucial for enhancing model performance, especially in
specialized contexts.</span></li>
<li class="c23 c38 li-bullet-1"><span class="c3">Hybrid Methodologies</span><span class="c1">: Integrating
rule-based compliance frameworks with LLM outputs can substantially enhance accuracy and reliability,
particularly in compliance- heavy scenarios.</span></li>
<li class="c64 c67 li-bullet-5"><span class="c3">Expansion of Benchmarks</span><span class="c1">: Extending
evaluations into other regulated domains such as healthcare, finance, and environmental law will provide
broader insights into model generalizability and robustness.</span></li>
</ul>
<p class="c2"><span class="c1"></span></p>
<ol class="c5 lst-kix_list_5-0" start="8">
<li class="c34 li-bullet-9">
<h1 style="display:inline"><span class="c6">Conclusion:</span></h1>
</li>
</ol>
<p class="c15"><span class="c1">This comprehensive benchmarking exercise rigorously assessed multiple
general-purpose Large Language Models across a structured array of legal cognitive tasks using Bloom’s
taxonomy. Our results underscore both substantial strengths and pronounced limitations of current LLMs,
particularly their struggles with nuanced, domain-specific regulatory</span></p>
<p class="c18"><span class="c1">reasoning. Recognizing these limitations, we are currently advancing our proprietary
model, Mike, with a dedicated and systematic training regimen explicitly structured around Bloom’s
taxonomy and specialized regulatory compliance datasets. This targeted approach positions Mike to overcome
many of the identified limitations, potentially enabling it to deliver advanced legal reasoning, precise
regulatory compliance analyses, and practical solutions</span></p>
<p class="c18"><span class="c1">tailored to specific industry contexts. Future developments will focus on enhancing
Mike’s capacity for interpretive depth, nuanced reasoning, and regulatory precision, setting a robust
foundation for its deployment in complex legal and regulatory environments.</span></p>
<p class="c9"><span class="c1"></span></p>
<h2 class="c18"><span class="c3">References:</span></h2>
<p class="c2"><span class="c3"></span></p>
<ul class="c5 lst-kix_list_1-0 start">
<li class="c0 li-bullet-6"><span class="c1">LEXam (2025), GitHub, HuggingFace.</span></li>
<li class="c0 li-bullet-5"><span class="c1">LegalBench (2023), GitHub, OpenReview.</span></li>
<li class="c0 li-bullet-1"><span class="c1">LLeQA (2023), GitHub, HuggingFace dataset.</span></li>
<li class="c0 li-bullet-5"><span class="c1">LexEval (2024), ArXiv, GitHub.</span></li>
</ul>
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