`Hallucinations in Large Language Models and Their Influence on Legal Reasoning: Examining the Risks of AI-Generated Factual Inaccuracies in Judicial Processes

Abstract

Legal frameworks rely on factual coherence, yet modern Large Language Models (LLMs) can generate content that contains spurious statements. Hallucinations, defined as fabricated or unverifiable information produced by AI, pose a significant threat to judicial processes when deployed without meticulous oversight. Risk emerges when judges, attorneys, and other legal professionals reference AI-generated text for evidence gathering or legal argument construction. Hallucinations can introduce distortions that are not grounded in any factual source, thereby undermining the integrity of legal argumentation. Recent advances in transformer architectures have improved language comprehension, though these very architectures also facilitate unsubstantiated extrapolations. Such unsubstantiated material can be difficult to detect, especially within dense legal documents, and may result in flawed case strategies or erroneous judgments. Persistent reliance on AI outputs invites a growing dependence on algorithms that lack genuine comprehension of legal precedents, statutes, and contextual nuances. This paper explores the phenomenon of hallucinations arising from state-of-the-art LLMs, focusing on their manifestation in legal applications and the resultant impact on legal reasoning. Analytical discussions center on the mechanisms that yield hallucinations, the challenges of verifying AI-generated text in complex legal contexts, and the implications for judicial integrity. Examination of these risks informs a deeper understanding of how AI might inadvertently compromise justice.