Modern AI Stack for Legal Applications: Techniques and Potential UseCases

Artificial Intelligence (AI) is reshaping the world as we know it. From revolutionizing industries to altering our daily lives, AI's impact is profound and far-reaching. In this article, we'll introduce you to the basics of AI, its various applications, and the potential it holds for the future. Let's embark on this enlightening journey into the world of AI.

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Retrieval

Legal documents are vast and dynamic. Large Language Models (LLMs) have a limited context window, making it challenging to capture the entirety of legal texts. This limitation birthed Retrieval Augmented Generation (RAG), which combines the strengths of retrieval and generation.

Embeddings

Embeddings convert text into mathematical vectors, placing similar texts closer in a mathematical space.

• Options: OpenAI, Cohere, Google. Specialized embeddings for legal texts might be necessary.
• Pros: Easy to start.
• Cons: Quality depends on the embeddings used.
• Cost: $

Taxonomies

Taxonomies, often visualized as knowledge graphs, provide a structured representation of legal texts.

• Pros: Enhances retrieval capabilities.
• Cons: Creating custom taxonomies can be complex.
• Cost: $$

Neural Retriever Models

These models, though smaller, are designed to fetch relevant texts by understanding the context.

• Pros: Efficient and context-aware.
• Cons: Requires AI expertise and infrastructure.
• Cost: $$$

Generation

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Generation is the realm where AI behemoths make their mark. Companies like OpenAI, Anthropic, Google,  Meta, and Inflection are all vying to craft an AI model that can cater to a myriad of tasks. The generation process can be enhanced and tailored using various techniques:

Prompt Engineering

Crafting specific prompts to guide AI outputs.

• Pros: Simple and often effective.
• Cons: Can't introduce new knowledge.
• Cost: $

Model Fine-tuning

Training an existing model on specific data to enhance its performance on particular tasks.

• Pros: Tailored outputs.
• Cons: Requires domain-specific data and special skillset.
• Cost: $$

Model Pre-training

Training models from scratch or further on domain-specific data.

• Pros: Highly specialized outputs.
• Cons: Resource-intensive and requires special skillset.
• Cost: $$$$

Feedback

Reinforcement Learning from Human Feedback (RLHF) refines AI models based on human feedback, ensuring alignment with human expectations.

• Pros: Continuous model improvement.
• Cons: Requires iterative feedback, humans can introduce bias & not easy to implement
• Cost: $$$

Legal UseCases and Techniques

For instance, Legal Research requires comprehensive retrieval capabilities, making all retrieval techniques relevant. Fine-tuning helps in generating summaries, while feedback refines the model's outputs.

Conclusion

The integration of AI within the legal domain signifies a profound shift in how we approach and manage legal processes. The techniques and tools discussed in this article highlight the potential of AI to augment our capabilities, offering more precision and efficiency in legal tasks. As we continue to refine and adapt these technologies, it's crucial to maintain a balance between innovation and ethical considerations. The journey ahead is filled with challenges and opportunities, and it's up to us to navigate this evolving landscape responsibly and thoughtfully.

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