Authors: Andrew Blair-Stanek, Nils Holzenberger, Benjamin Van Durme
Published on: November 16, 2023
Impact Score: 7.4
Arxiv code: Arxiv:2311.09693
Summary
- What is new: Fine-tuning smaller LLMs leads to near-perfect performance on legal text handling tasks, demonstrating foundational LLMs lack certain domain-specific behaviors without expert intervention.
- Why this is important: Leading LLMs like GPT-4 perform poorly at basic legal text tasks expected of them in zero-shot scenarios.
- What the research proposes: The introduction of a benchmark for legal text tasks and fine-tuning LLMs on these tasks to improve performance.
- Results: Fine-tuned LLMs achieved near-perfect performance on the benchmark, significantly improving their usability for legal practice.
Technical Details
Technological frameworks used: Fine-tuning methodologies for LLMs
Models used: GPT-4, Claude, PaLM 2
Data used: Legal texts including witness depositions and contract subsections
Potential Impact
Legal tech companies, law firms, paralegal services
Want to implement this idea in a business?
We have generated a startup concept here: LegalAssistAI.
Leave a Reply