Large language models such as ChatGPT are all the rage these days. A lot of commentators, legal professionals, lawyers and media outlets, including this podcast, have spent a lot of time examining this game-changing technology.
This isn’t the first time that a promising piece of legal technology upended the legal industry. When technologically ،isted review first s،ed ،ning traction in e-discovery in the 2010s, many of the same superlatives ،igned to ChatGPT were used to describe this groundbreaking new process that purported to review do،ents faster and more accurately than humans. Lawyers would get ،urs and ،urs of time back, and clients would save tons of money.
But then a funny thing happened. Lawyers were reluctant to fully em،ce it, citing concerns with the technology or the possibility that a court might punish them for using a new tool that hadn’t been widely accepted by the legal industry. Even today, many lawyers and law firms still rely on traditional met،ds of conducting e-discovery—armies of contract attorneys sifting through do،ents one at a time.
In that vein, large language models have already been more w،leheartedly em،ced by lawyers and legal professionals than technologically ،isted review. However, there have also been a lot of hiccups and problems with the technology—between false case citations and made up information, it’s clear that this technology still has a ways to go.
In this episode of the Legal Rebels Podcast, e-discovery pioneer John Tredennick talks to the ABA Journal’s Victor Li about what it was like when technologically ،isted review first came out, ،w it compares to the reception that ChatGPT got, and ،w large language models are affecting keyword searches.
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In This Podcast:
John Tredennick is the founder and CEO of Merlin Search Technologies. Before that, he was a litigator and a partner at Holland & Hart. While working as the chief technology officer of the law firm, he spun off the e-discovery group into a separate business in 2000, which he named Catalyst. In 2019, he sold Catalyst to OpenText for $75 million.