Pith. sign in

REVIEW 3 cited by

Better Call GPT, Comparing Large Language Models Against Lawyers

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2401.16212 v1 pith:27FFU2KA submitted 2024-01-24 cs.CY cs.CL

Lauren Martin , Nick Whitehouse , Stephanie Yiu , Lizzie Catterson , Rivindu Perera (Onit AI Centre of Excellence) This is my paper · ORCID
classification cs.CYcs.CL
keywords legalllmscontractcostlawyersmodelsaccuracyefficiency
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This paper presents a groundbreaking comparison between Large Language Models and traditional legal contract reviewers, Junior Lawyers and Legal Process Outsourcers. We dissect whether LLMs can outperform humans in accuracy, speed, and cost efficiency during contract review. Our empirical analysis benchmarks LLMs against a ground truth set by Senior Lawyers, uncovering that advanced models match or exceed human accuracy in determining legal issues. In speed, LLMs complete reviews in mere seconds, eclipsing the hours required by their human counterparts. Cost wise, LLMs operate at a fraction of the price, offering a staggering 99.97 percent reduction in cost over traditional methods. These results are not just statistics, they signal a seismic shift in legal practice. LLMs stand poised to disrupt the legal industry, enhancing accessibility and efficiency of legal services. Our research asserts that the era of LLM dominance in legal contract review is upon us, challenging the status quo and calling for a reimagined future of legal workflows.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 10 citations worldwide. Full citation record

  1. DECODEM: Data Extraction from Corporate Organizational Documents via Enhanced Methods

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Frontier LLMs accurately extract many corporate governance variables from charters and bylaws on the new DECODEM benchmarks, but complex provisions and label noise remain the main error sources.

  2. Using Large Language Models for Legal Decision-Making in Austrian Value-Added Tax Law: An Experimental Study

    cs.CL 2025-07 conditional novelty 6.0 of 10

    RAG-enhanced LLMs slightly outperform fine-tuned LLMs on Austrian/EU VAT questions, but not significantly, and neither approach is ready for full automation.

  3. When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance

    cs.CL 2025-07 conditional novelty 4.0 of 10

    A literature review that classifies LLM-for-law research using a dual-lens taxonomy of Toulmin argumentation components and legal practitioner roles.

Pith tools