Large Language Model (LLM)
Definition
A large language model is a neural network trained on vast text corpora that can understand and generate human language. LLMs power the natural language capabilities of legal AI tools, enabling them to read contracts, draft documents, answer research questions, and summarize complex legal materials in plain language.
Large language models like GPT-4, Claude, and others are trained on billions of words of text, learning statistical patterns in language that allow them to generate coherent, contextually appropriate responses. They do not store or retrieve specific documents; they learn relationships between words, concepts, and reasoning patterns.
In legal technology, LLMs serve as the intelligence engine behind a wide range of capabilities. They can parse dense contract language, identify relevant legal concepts in a set of facts, generate first drafts of documents, and explain complex legal principles in accessible terms. However, their effectiveness in legal applications depends entirely on how they are deployed: a raw LLM without access to verified legal data will produce fluent but unreliable output.
Law firms evaluating AI platforms should understand that the choice of LLM is only one factor in system quality. The surrounding architecture, including retrieval systems, citation verification, security controls, and fine-tuning on legal data, often matters more than which base model a platform uses. The best legal AI systems are model-agnostic, able to leverage the strengths of different LLMs for different tasks.
How Irys approaches this
Irys leverages multiple large language models, selecting the optimal model for each task and wrapping every model interaction with legal-specific retrieval and verification layers.
Related terms
AI Hallucination in Legal
An AI hallucination occurs when a language model generates text that appears authoritative but is factually incorrect, such as fabricating case citations, inventing statutes, or misrepresenting holdings. In legal practice, hallucinations carry professional responsibility implications because lawyers have a duty to verify the accuracy of every authority they cite.
AI ConceptsToken Limit
A token limit is the maximum number of tokens (word fragments or characters) that an AI model can process in a single request, encompassing both the input and the output. In legal applications, token limits determine how much source material the AI can analyze at once and how long its generated responses can be.
AI ConceptsAI Context Window
The context window is the maximum amount of text an AI model can process in a single interaction, measured in tokens. A larger context window allows the model to consider more documents, longer contracts, or more extensive case histories simultaneously, which directly impacts the quality and completeness of its legal analysis.
AI ConceptsFine-Tuning Legal AI
Fine-tuning is the process of further training a pre-trained language model on a specialized dataset to improve its performance in a specific domain. In legal AI, fine-tuning on legal corpora teaches the model legal reasoning patterns, citation formats, jurisdictional distinctions, and the precision that legal analysis demands.
See Large Language Model (LLM) in action
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