Report · Page 61

On the Opportunities and Risks of Foundation Models
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access to justice by providing information tailored to a client’s particular needs [Cabral et al. 2012;
Brescia et al. 2014; Queudot et al. 2020; Westermann et al. 2019].
Once a client speaks with an attorney, prior to civil litigation, the attorney may seek to avoid a
costly trial. At this stage, they can rely on foundation models to evaluate contracts, review terms
of service, find relevant patents, and conduct other pre-litigation processes in order to ensure
that their clients are at an advantage [Betts and Jaep 2017; Elwany et al. 2019; Lippi et al. 2019;
Lee and Hsiang 2019; Hendrycks et al. 2021c; Hegel et al. 2021]. Notably, recent work has both
described the challenges and benefits of using foundation models for contract review [Leivaditi
et al. 2020; Hegel et al. 2021; Hendrycks et al. 2021c]. In addition to reviewing and drafting legal
documents, client interactions and documents can be translated to reduce costs and barriers to the
provision of legal services [Cuéllar 2019]. But translation of legal documents requires precision
and an understanding of highly technical language, which makes collecting training data costly.
Additionally, translating client statements or trial proceedings often requires an understanding of
local dialects and language. This, too, makes it difficult to collect enough ground truth translation
data to train on. As a result, traditional supervised methods rarely achieve the level of accuracy
required in the legal domain [Vieira et al. 2020]. Foundation models may improve performance in
this area over fully supervised mechanisms by adapting quickly in these low-resource contexts.
During litigation, foundation models can help lawyers to conduct legal research, draft legal lan-
guage, or assess how judges evaluate their claims [Zheng et al. 2021; Huang et al. 2021b; Ostendorff
et al. 2021; Vold and Conrad 2021; Chalkidis et al. 2020, 2019]. This could potentially reduce the
costs of and improve legal services. For example, recent work has utilized pretrained models for the
recommendation of relevant citations and holding statements when writing legal texts [Zheng et al.
2021; Huang et al. 2021b; Ostendorff et al. 2021]. Other work uses pretrained models for improved
legal question answering to power commonly used legal search engines and help lawyers conduct
legal research [Vold and Conrad 2021]. A wide variety of work has also examined automated
contract drafting and review, a task that could similarly benefit from foundation models [Hendrycks
et al. 2021c; Betts and Jaep 2017]. Perhaps most compelling, foundation models may help assist
lawyers generate legal briefs (written arguments). The models might find novel arguments or
identify problems in attorney-written portions of the brief. For example, Tippett et al. [2021] predict
the outcome of a legal proceeding based on features extracted from the filed briefs. Foundation
models can be leveraged to use raw language as inputs rather than extracted features. This might
provide attorneys with more informative recommendations as to how their brief could be improved
to ensure a favorable outcome.
After opening and reply briefs are filed, parties then begin the discovery process, which has
already used simple machine learning models for the better part of a decade [Grossman and
Cormack 2010]. Attorneys use these systems to label whether a document should be produced to
the opposing party. The documents