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Ilya A. Strebulaev is The David S. Lobel Professor of Private Equity and Professor of Finance at Stanford Graduate School of Business, where he has been a faculty member since 2004, and a research associate at the National Bureau of Economic Research. He also is the founder and director of the Stanford GSB Venture Capital Initiative. He graduated from the London Business School with a doctorate in finance. He also holds degrees from Lomonosov Moscow State University (BSc Economics) and the New Economic School, Moscow (MA Economics).
Professor Strebulaev is an expert in corporate finance, venture and angel capital, innovation financing, corporate innovation, private equity, and financial decision-making. His work has been widely published in leading academic journals, including the Journal of Finance, the Review of Financial Studies, and the Journal of Financial Economics. He has been awarded a number of prestigious academic awards, including the First Paper Prize of the Brattle Award for the best corporate paper published in the Journal of Finance, the Fama-DFA Prize for the best asset pricing paper published in the Journal of Financial Economics , and the Trefftzs Award by the Western Finance Association. His research has also been featured in a variety of media, including the New York Times, the Wall Street Journal and Harvard Business Review .
His most recent research has examined many aspects of the venture capital industry. In the largest ever survey of VCs to date, he and his co-authors analyze all the aspects of decision-making by venture capitalists. He and his co-author developed a valuation framework of private VC-backed companies. In applying this framework to the valuation of highly valued VC-backed companies (called “unicorns”), hey found that these companies on average are overvalued by 50% and that many of the so-called unicorns lose their unicorn status once their fair value is taken into consideration. He has also recently researched the decision making and organizational structure of corporate VC units.
Professor Strebulaev teaches the MBA, MSx, PhD, and executive education programs, and has been awarded the Stanford MBA Distinguished Teaching Award, the Sloan Teaching Excellence Award, as well as the inaugural Masters in Management Best Teacher Award at the London Business School. He developed an MBA-level course on Angel and Venture Capital that he has been teaching for more than ten years. The course enables the students to study many aspects of innovation financing at various stages, including decision making, attracting venture and angel investments, negotiating contractual terms, valuing VC-backed companies, and analyzing the performance of venture capital funds. Recently, he also developed a course on the private equity industry that covers all aspects of the organization and design of PE firms and funds, as well as the relationship between general partners of these funds and their investors, limited partners.
Professor Strebulaev has also led many workshops and executive sessions on new innovation trends, venture capital, the ecosystem of Silicon Valley, corporate innovation, and strategic decision making for senior business and government leaders around the world. He also has been consulting companies and investors around the world on valuation of VC-backed companies, selection of VC investments and managers, and portfolio allocation. He also serves as an expert witness in litigation matters.
When not teaching or doing research, Ilya enjoys spending time with his family, reading, traveling, listening to classical music, and appreciating fine wine and art.
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A new study finds disturbing and pervasive errors among three popular models on a wide range of legal tasks.
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In May of last year, a Manhattan lawyer became famous for all the wrong reasons. He submitted a legal brief generated largely by ChatGPT. And the judge did not take kindly to the submission. Describing “an unprecedented circumstance,” the judge noted that the brief was littered with “bogus judicial decisions . . . bogus quotes and bogus internal citations.” The story of the “ChatGPT lawyer” went viral as a New York Times story, sparking none other than Chief Justice John Roberts to lament the role of “hallucinations” of large language models (LLMs) in his annual report on the federal judiciary.
Yet how prevalent are such legal hallucinations, really?
The legal industry is on the cusp of a major transformation, driven by the emergence of LLMs like ChatGPT, PaLM, Claude, and Llama. These advanced models, equipped with billions of parameters, have the ability not only to process but also to generate extensive, authoritative text on a wide range of topics. Their influence is becoming more evident across various aspects of daily life, including their growing use in legal practices.
A dizzying number of legal technology startups and law firms are now advertising and leveraging LLM-based tools for a variety of tasks, such as sifting through discovery documents to find relevant evidence, crafting detailed legal memoranda and case briefs, and formulating complex litigation strategies. LLM developers proudly claim that their models can pass the bar exam. But a core problem remains: hallucinations, or the tendency of LLMs to produce content that deviates from actual legal facts or well-established legal principles and precedents.
Until now, the evidence was largely anecdotal as to the extent of legal hallucinations. Yet the legal system also provides a unique window to systematically study the extent and nature of such hallucinations.
In a new preprint study by Stanford RegLab and Institute for Human-Centered AI researchers, we demonstrate that legal hallucinations are pervasive and disturbing: hallucination rates range from 69% to 88% in response to specific legal queries for state-of-the-art language models. Moreover, these models often lack self-awareness about their errors and tend to reinforce incorrect legal assumptions and beliefs. These findings raise significant concerns about the reliability of LLMs in legal contexts, underscoring the importance of careful, supervised integration of these AI technologies into legal practice.
Hallucination rates are alarmingly high for a wide range of verifiable legal facts. Yet the unique structure of the U.S. legal system – with its clear delineations of hierarchy and authority – allowed us to also understand how hallucination rates vary along key dimensions. We designed our study by constructing a number of different tasks, ranging from asking models simple things like the author of an opinion to more complex requests like whether two cases are in tension with one another, a key element of legal reasoning. We tested more than 200,000 queries against each of GPT 3.5, Llama 2, and PaLM 2, stratifying along key dimensions.
First, we found that performance deteriorates when dealing with more complex tasks that require a nuanced understanding of legal issues or interpretation of legal texts. For instance, in a task measuring the precedential relationship between two different cases, most LLMs do no better than random guessing. And in answering queries about a court’s core ruling (or holding), models hallucinate at least 75% of the time. These findings suggest that LLMs are not yet able to perform the kind of legal reasoning that attorneys perform when they assess the precedential relationship between cases—a core objective of legal research.
Second, case law from lower courts, like district courts, is subject to more frequent hallucinations than case law from higher courts like the Supreme Court. This suggests that LLMs may struggle with localized legal knowledge that is often crucial in lower court cases, and calls into doubt claims that LLMs will reduce longstanding access to justice barriers in the United States.
Third, LLMs show a tendency to perform better with more prominent cases, particularly those in the Supreme Court. Similarly, performance is best in the influential Second and Ninth Circuits, but worst in circuit courts located in the geographic center of the country. These performance differences could be due to certain cases being more frequently cited and discussed, thus being better represented in the training data of these models.
Fourth, hallucinations are most common among the Supreme Court’s oldest and newest cases, and least common among later 20th century cases. This suggests that LLMs’ peak performance may lag several years behind current legal doctrine, and that LLMs may fail to internalize case law that is very old but still applicable and relevant law.
Last, different models exhibit varying degrees of accuracy and biases. For example, GPT 3.5 generally outperforms others but shows certain inclinations, like favoring well-known justices or specific types of cases. When asked who authored an opinion, for instance, GPT 3.5 tends to think Justice Joseph Story wrote far more opinions than he actually did.
Another critical danger that we unearth is model susceptibility to what we call “contra-factual bias,” namely the tendency to assume that a factual premise in a query is true, even if it is flatly wrong. For instance, if one queried, “Why did Justice Ruth Bader Ginsburg dissent in Obergefell ?” (the case that affirmed a right to same-sex marriage), a model might fail to second-guess whether Justice Ginsburg in fact dissented.
This phenomenon is particularly pronounced in language models like GPT 3.5, which often provide credible responses to queries based on false premises, likely due to its instruction-following training. This tendency escalates in complex legal scenarios or when dealing with lower court cases. Llama 2, on the other hand, frequently rejects false premises, but sometimes mistakenly denies the existence of actual cases or justices.
Relatedly, we also show that models are imperfectly calibrated for legal questions. Model calibration captures whether model confidence is correlated with the correctness of answers. We find some divergence across models: PaLM 2 and ChatGPT (GPT 3.5) show better calibration than Llama 2. Yet, a common thread across all models is a tendency towards overconfidence, irrespective of their actual accuracy. This overconfidence is particularly evident in complex tasks and those pertaining to lower courts, where models often overstate their certainty, especially in well-known or high-profile legal areas.
The implications of these findings are serious. Today, there is much excitement that LLMs will democratize access to justice by providing an easy and low-cost way for members of the public to obtain legal advice. But our findings suggest that the current limitations of LLMs pose a risk of further deepening existing legal inequalities, rather than alleviating them.
Ideally, LLMs would excel at providing localized legal information, effectively correct users on misguided queries, and qualify their responses with appropriate levels of confidence. However, we find that these capabilities are conspicuously lacking in current models. Thus, the risks of using LLMs for legal research are especially high for:
In essence, the users who would benefit the most from legal LLM are precisely those who the LLMs are least well-equipped to serve.
There is also a looming risk of LLMs contributing to legal “ monoculture .” Because LLMs tend to limit users to a narrow judicial perspective, they potentially overlook broader nuances and diversity of legal interpretations. This is substantively alarming, but there is also a version of representational harm: LLMs may systematically erase the contributions of one member of the legal community, such as Justice Ginsburg, by misattributing them to another, such as Justice Story.
Much active technical work is ongoing to address hallucinations in LLMs. Yet addressing legal hallucinations is not merely a technical problem. We suggest that LLMs face fundamental trade-offs in balancing fidelity to training data, accuracy in responding to user prompts, and adherence to real-world legal facts. Thus, minimizing hallucinations ultimately requires normative judgments about which type of behavior is most important, and transparency in these balancing decisions is critical.
While LLMs hold significant potential for legal practice, the limitations we document in our work warrant significant caution. Responsible integration of AI in legal practice will require more iteration, supervision, and human understanding of AI capabilities and limitations.
In that respect, our findings underscore the centrality of human-centered AI. Responsible AI integration must augment lawyers, clients, and judges and not, as Chief Justice Roberts put it, risk “dehumanizing the law.”
Matthew Dahl is a J.D./Ph.D. student at Yale University and graduate student affiliate of Stanford RegLab.
Varun Magesh is a research fellow at Stanford RegLab.
Mirac Suzgun is a J.D/Ph.D. student in computer science at Stanford University and a graduate student fellow at Stanford RegLab.
Daniel E. Ho is the William Benjamin Scott and Luna M. Scott Professor of Law, Professor of Political Science, Professor of Computer Science (by courtesy), Senior Fellow at HAI, Senior Fellow at SIEPR, and Director of the RegLab at Stanford University.
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Search for available job openings at Stanford Medicine. ... California, posted: 06/05/2024 Save Job; Research Program Manager Stanford, California, posted: 02/25/2024 Save Job; Previous Next. Enter number to jump to a different page. You are currently on page 1 / 14. ... Remote Eligible (5) Schedule. Full-time (136) Part-time (4) Grade. A11 (1 ...
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Search for available job openings at Stanford Medicine. Skip to main content Skip to Search Results Skip to Search Filters. Begin your journey with us - join our Early Career Talent Program! ... 04/14/2024 Save Job; Research Assistant Clinical Research Coordinator Stanford, California, posted: 07/15/2024 Save Job; Research Cancer Clinical ...
This position is a 6-month full-time contract role that is funded by an extramural grant. Under the general supervision of the research manager, the Research Data Analyst will take and pass human subjects training modules, read-in and manage large-scale secondary datasets (e.g., US Vital Statistics), and conduct intensive longitudinal data analysis consistent with the epidemiologic aims of the ...
Open Postdoctoral Positions. The Ahmad lab is seeking postdoctoral fellows interested in working on brain imaging and hearing loss. Our research team is looking for a postdoctoral scholar interested in studying the use of pain medicines and/or substance use disorders among pregnant and postpartum individuals.
Research Accountant 1. Stanford University. Remote in Redwood City, CA. $60,000 - $96,000 a year. Full-time. Weekends as needed + 1. Work collaboratively with Institutional Representatives, Accounting Associates, Administrators, and AR staff to clarify financial terms and ensure correct…. Posted 4 days ago ·. More...
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Senior Assistant Director of Admission for Regional Outreach. Stanford University. Remote in Stanford, CA. $54,000 - $97,000 a year. Full-time. The Office of Undergraduate Admission assembles a freshman class of 1,700 students and a transfer class of approximately 25-40 students each year. We strive….
The HAI Research and Financial Analyst provides research administration and finance support to the Institute for Human-Centered Artificial Intelligence (HAI). This person will work under minimal supervision to manage the proposal preparation and/or post award activities on grants, contracts, program projects, and federal grants, both routine ...
Remote in Stanford, CA. $82,000 - $126,000 a year. Full-time. Extended hours. Research and Grants Manager to work under minimal supervision on all financial aspects of the Principal Investigators assigned to the work unit they support. Posted 30+ days ago ·.
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The Division of Pediatric Surgery at Stanford University School of Medicine is seeking an Assistant Clinical Research Coordinator to perform administrative support duties related to the collection of clinical data and/or the coordination of clinical studies. ... geographic location and external market pay for comparable jobs. At Stanford ...
Manage research project databases, develop flow sheets and other study related documents, and complete study documents/case report forms. • Ensure compliance with research protocols, and review and audit case report forms for completion and accuracy with source documents.
Bio. Ilya A. Strebulaev is The David S. Lobel Professor of Private Equity and Professor of Finance at Stanford Graduate School of Business, where he has been a faculty member since 2004, and a research associate at the National Bureau of Economic Research. He also is the founder and director of the Stanford GSB Venture Capital Initiative.
Job Summary. DATE POSTED Jan 28, 2022. Schedule Full-time. Job Code 4189. Employee Status Fixed-Term. Grade E. Requisition ID 93027. Thanks for your interest in the Research Assistant position. Unfortunately this position has been closed but you can search our 606 open jobs by clicking here .
Beginning 1972, three physicists at Stanford Research Institute (now known as SRI International)--Harold Puthoff, Edwin May, and Russell Targ--initiated free-response remote viewing ...
In a new preprint study by Stanford RegLab and Institute for Human-Centered AI researchers, we demonstrate that legal hallucinations are pervasive and disturbing: hallucination rates range from 69% to 88% in response to specific legal queries for state-of-the-art language models. Moreover, these models often lack self-awareness about their ...
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