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27 I L T A N E T . O R G Feature engineering is a key step involving selecting and transforming variables the model will use to make predictions. Important features might include the judge's ruling history, tendencies of the jurisdiction, legal precedents, and case details. Natural language processing (NLP) techniques extract meaningful patterns from the text in case filings and legal arguments. Handling class imbalance in datasets of judicial decisions is essential because there might be an uneven number of cases won versus lost. Techniques like resampling the data, using algorithms that handle imbalance well (like ensemble methods), and using appropriate EMERGING APPLICATIONS The potential of artificial intelligence in the legal field extends beyond document review, encompassing predictive analytics and advanced research tools in addition to other legal practice areas. PREDICTIVE ANALYTICS IN LITIGATION Predictive analytics uses statistical methods and machine learning algorithms (ML) to forecast outcomes based on historical data. Predictive analytics can help lawyers develop strategies by providing insights into the chances of winning a case by adopting specific strategies. Techniques like incorporating retrieval- augmented generation (RAG), where the model accesses external databases to verify facts before generating the final responses, assist with reducing hallucinations. Also, adjusting how the model creates text can help reduce inaccuracies in some cases. Several other prompt engineering techniques are useful for addressing different types of legal tasks beyond this article's scope. LEGAL-SPECIFIC FINE-TUNING STRATEGIES Customizing large language models through fine-tuning legal data enhances their capabilities to meet the legal field's specific linguistic and practical requirements. Fine-tuning legal data to customize LLMs involves further training the model on domain-specific data like laws, case judgments, and legal documents from particular legal practice areas. For the best outcome, the training data needs to be substantial and represent the variety of language used in legal documents. It's essential that the data is current and reflects the latest laws and regulations, especially for models focused on specific jurisdictions. Managing model drift is an ongoing concern, especially in regulatory compliance, where laws change more frequently. Regularly checking the model's outputs and retraining it with new data helps maintain accuracy for more extended time frames. Using recent cases or laws to validate the model's predictions can detect changes in performance, prompting necessary updates. FEATURES

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