The unveiling of OpenAI’s GPT3, or Generative Pretrained Transformer 3, has generated significant enthusiasm in the AI community. Boasting an impressive 175 billion parameters, GPT3 stands as the most advanced language processing AI model currently available, able to produce text that closely mimics human writing in various contexts. GPT3 for legal research could be a game-changer. The overwhelming paperwork that legal professionals have to work with, such as contracts, legal documents, and case law, can be easy with GPT3. Its capability to generate and process text at a large scale makes it a wonderful tool for this work. In this article, we are analyzing does GPT-3 have the capability to replace a lawyer.
GPT3 has the potential to revolutionize the legal industry with its cutting-edge language processing capabilities. Here are a few examples of how GPT3 could be utilized in legal research:
Document drafting and review: GPT3 for legal research could generate draft documents and contracts, saving legal professionals valuable time and reducing potential errors. It could also be used to review existing documents for accuracy and completeness, ensuring that all legal documents are legally compliant.
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Case law analysis: with its ability to process and analyze large amounts of text, it could be trained on a significant collection of case law to aid legal professionals in identifying and locating relevant cases and established legal principles with speed and precision. This could be especially useful for junior lawyers or those working in specialized areas of law, allowing them to stay up-to-date on the latest legal developments.
Legal research: GPT3 could assist with legal research by generating summaries of relevant legal documents and case law. This could help lawyers save time and stay informed about recent legal developments. GPT3 could be trained on a large dataset of legal documents to identify the sentiment of the text. This could be used to identify the potentially problematic language in contracts or to analyze the sentiment of case law to see if it is favorable or unfavorable towards a particular argument.
Legal translation: Can assist with legal translation by producing accurate legal documents from one language to another. This could be especially useful for international law firms or those dealing with cross-border legal matters.
Whether GPT3 will be useful in practice has a complex answer. In some tasks, it has the potential to be highly useful due to its ability to match state-of-the-art results without the need for fine-tuning. This makes it easier to deploy for tasks that can achieve similar results as other models, reducing the need for expensive fine-tuning. However, there are also limitations to its application, which should be taken into consideration.
One potential barrier to using GPT3 for legal research is privacy concerns. Currently, the only way to access GPT3 is through an API provided by OpenAI, which may not be suitable for organizations with strict privacy requirements or those that require deep integration within their systems. Additionally, there would still be privacy challenges even if organizations had direct access to the model. For example, clients may legally be obligated to delete data after finishing a project. If that data is reconstructible from the trained model, it could lead to legal troubles.
Another issue is that while GPT3 can be adapted to new tasks without fine-tuning, it still needs to be primed with examples of what you want it to do. To avoid priming it every time it is used, the state of the primed model must be saved, raising questions about whether anything can be learned about the examples used to prime the model by inspecting its state. Furthermore, if fine-tuning were to be done, the problem would become even more severe as language models like GPT3 can generate text that looks exactly like the documents it was trained on.
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Privacy concerns are not unique to GPT3 for legal research, but it is a known issue among popular deep learning systems. As demonstrated by TensorFlow’s library, solutions such as differential privacy can be applied to GPT3 and other models, but additional work would be required.
Another limitation of GPT3 and similar technology is the source of the training data. In the legal field, it is important to know whether the model has been exposed to the types of contracts it will be used for. While we can be certain that GPT3 has not been exposed to private data, it may not be as effective on documents that are not publicly available. It is also unclear if OpenAI included EDGAR data when training GPT3, an important public source of contracts.
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This is not a fundamental limitation of the technology. Still, retraining or continuing to train a model like GPT3 to include more domain-specific data can be expensive. It may not be possible if OpenAI does not release the model itself.
Additionally, most of the data GPT3 is trained on is in English. Further training with non-English data would be necessary for the model to be useful for other languages.
Another potential barrier with GPT3 for legal research is the concern of bias. As it is trained on a subset of the internet, it may have inherent biases present in the training data. This could include racial biases, biases based on gender, or others. While the amount of training data does mitigate this to some extent, it is still a significant issue and one of the main challenges OpenAI is working to address as they release the model for use by others.
The traditional solution would be to try to “fix” the training data by tackling bias in AI, but this is not practical in this case. Instead, OpenAI is exploring approaches that filter results to eliminate bias. It is important to note that this model can generate potentially offensive language.
Additionally, bias can manifest in other ways, such as the model being biased towards US law if it is primarily trained on US legal resources. This could be a problem for legal applications in other countries such as the UK or Canada.
The question of what it means for patent attorneys or agents to “supervise” technology like GPT3 is complex. Scholars who have studied the issue have suggested that lawyers take several steps to ensure they are properly supervising AI.
For example, legal ethics professor Roy Simon recommends that lawyers who use AI:
The first step, hiring an expert, may be easier for large law firms with funding to hire IT consultants. The second step, learning about the AI product, can be achieved through continuing legal education or conversations with software vendors. This article will help you find insights on Implementing Artificial Intelligence in your business and measuring its impact.
The third step, double-checking the output of the AI product, is crucial, but it may reduce some of the tool’s utility. However, disclosing which sections of the legal documents were authored by the AI and which by the attorney may address some potential harms associated with AI-drafted applications. It is important to note that some less scrupulous attorneys may sign legal documents that they have not thoroughly reviewed, which would violate the “competence” requirement of overlooking government agencies.
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Oversight of GPT3 for legal research is crucial because of the potential for errors in the output, which can greatly impact the accuracy of a patent specification or claim. Additionally, it is important due to the tendency of AI tools to reflect the biases present in our society. GPT3 is trained on datasets that include text from websites like Reddit, where users may openly post racist, sexist, and homophobic content, and therefore, the tool may reflect that bias in its outputs.
GPT3’s creators acknowledge the existence of racial and gender bias in the tool. They have found through tests that it is more likely to write sentences about women that focus on their appearance or sexualize them. They have also found that the tool’s output for prompts involving Black people had consistently low sentiment value. These results highlight the need for a more sophisticated analysis of the relationship between sentiment, entities, and input data. Practitioners in the patent field need to be aware of these potential biases and be vigilant in reviewing the output of GPT3 to ensure that it does not introduce any bias in the patent claim and specification.
Harvey, a startup that aims to be a “copilot for lawyers,” has come out of cover with $5 million in funding from the OpenAI Startup Fund. Founded by Gabriel Pereyra, a former research scientist at DeepMind, Google Brain, and Meta AI, and Winston Weinberg, a former attorney at O’Melveny & Myers, Harvey uses large language models to understand the user’s intent and produce the desired outcome for legal tasks. Instead of using multiple specialized tools for various legal activities, Harvey offers a single, simple interface for all legal workflows.
Harvey is a technology startup that aims to assist lawyers in their tasks by simplifying their workflow. It uses advanced language models to understand the user’s intent and produce the desired outcome. Harvey eliminates the need for manual revision of legal documents or conducting research by providing a simple, user-friendly interface for lawyers to input their tasks and receive the desired outcome. For example, Harvey can answer queries such as “Explain the distinction between an employee and an independent contractor in the Fourth Circuit.” Rather than using multiple specialized tools for different legal activities, Harvey provides a single, streamlined solution for all legal workflows.
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According to Gabriel Pereyra, the founder of Harvey, the company goes to great lengths to ensure the privacy and security of its client’s data. This includes anonymizing user data and destroying it after a set period. Users also have the option to request the deletion of their data at any time. It is also claimed that Harvey does not “cross-contaminate” data between customers. However, there is some opposition to using AI-powered tools like Harvey in the legal industry. Other companies similar to Harvey, such as Casetext, Klarity, and Augmented leverage AI, also assist with legal research, contract review, and summarizing legal documents in plain language.
Despite the potential barriers and limitations, GPT3’s ability to generate human-like text and adapt to various tasks without extensive fine-tuning makes it a highly promising tool for the legal industry. AI continues to evolve, and the use of GPT3 for legal research (and other advanced language processing models) in the legal field is likely to become increasingly prevalent, streamlining processes and improving efficiency for legal professionals.
It is important to note that while GPT-3 has the potential to significantly improve legal research, it is not a substitute for the expertise and judgment of a licensed attorney. It should only be used to assist in legal research and should not be relied upon for making legal decisions. GPT-3 in legal research raises several ethical and legal questions, such as privacy concerns and potential bias in the model’s outputs. As such, the use of GPT-3 in legal research should be carefully monitored and regulated to ensure that it is used responsibly and ethically.