How GPT and other large language models could change e-discovery
Jul 19, 2023
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Exploring the transformative power of GPT and other large language models in e-discovery, including their potential impact on job roles. Discussing the mixed reactions and concerns surrounding their use, as well as their efficiency and accuracy. Highlighting the potential implications for the e-discovery industry and the need for more efficient discovery methods.
Large language models like GPT have the potential to revolutionize how lawyers analyze and understand document collections in the discovery process.
Merlin's Search Technologies is working on integrating large language models into document analysis and search to enhance search efficiency and accuracy in e-discovery.
Deep dives
The Rise and Reception of Large Language Models in the Legal Industry
Large language models like GPT have gained popularity in the legal profession and are seen as game-changers. However, just like the initial reception of technology-assisted review (TAR) in electronic discovery, there has been some hesitation in fully embracing these models. Lawyers have expressed concerns about the technology and potential acceptance within the legal industry, leading to a continued reliance on traditional methods. While large language models have been more wholeheartedly embraced than TAR, there have been challenges and issues such as false case citations and inaccurate information. Overall, there is recognition of the potential of large language models but also a need for further development and refinement.
The Evolution of Discovery and the Impact of Technology
The discovery process has evolved from paper-based document review in the early days to the digital age where electronic discovery became necessary. Previously, lawyers manually reviewed documents, but as the volume of digital content increased, technology-assisted solutions like TAR emerged. TAR promised to review documents faster and more accurately than humans, saving time and costs. However, its adoption faced resistance due to concerns about technology and court acceptance. The introduction of large language models has also impacted the keyword search approach in e-discovery. As technology advances, there is a growing need for more efficient and effective discovery methods.
The Power and Prominence of Large Language Models
Large language models, such as GPT and other variants, have captured significant attention due to their transformative capabilities. These models can generate accurate and informative summaries, provide context, and answer complex legal questions. While there have been concerns about hallucinations or false information, ongoing research and improvements are addressing these challenges. Moreover, integrating large language models into the discovery process has the potential to revolutionize how lawyers analyze and understand document collections. By leveraging the power of these models, it becomes possible to move from traditional search hits to meaningful discovery answers.
Merlin's Search Technologies and the Future of Discovery
Merlin's Search Technologies is at the forefront of developing a comprehensive platform that harnesses the capabilities of large language models for the discovery process. Their focus is on building a framework that seamlessly integrates these models into document analysis and search. With innovative algorithms, they are working to enhance search efficiency, provide accurate results, and facilitate better analysis of documents. By leveraging AI and machine learning, Merlin envisions a future where lawyers can focus on higher-level tasks, such as strategy and counseling, while technology handles the labor-intensive aspects of document review.
When technologically assisted review first started gaining traction in e-discovery in the 2010s, many of the same superlatives assigned to ChatGPT were used to describe this groundbreaking new process that purported to review documents faster and more accurately than humans. Lawyers would get hours and hours of time back, and clients would save tons of money.
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