arXiv:2606.23716v1 Announce Type: cross Abstract: Legal AI benchmark research frequently invokes the assumption that large language models can improve access to justice, including for people who cannot access lawyers in order to understand and exercise their legal rights. We argue that current benchmarks are not equipped to support this assumption because they evaluate legal reasoning over inputs that have already been preprocessed by legal experts, which measures the upper bound of model performance. Access to justice depends on a lower bound: how models perform when inputs come from pro se l
The rapid advancement of large language models is leading to increased scrutiny of their real-world applicability, particularly in sensitive areas like legal assistance.
This item highlights a critical flaw in current AI benchmark evaluations for legal applications, indicating that models may not perform as well as expected for unrepresented individuals.
The focus for legal AI development will likely shift towards benchmarks that incorporate 'pro se' inputs, moving beyond expert-preprocessed data to better serve access to justice initiatives.
- · Legal AI benchmark developers
- · Generative AI companies focusing on robust, real-world data
- · Pro se litigants
- · AI models trained exclusively on expert-curated legal data
- · Legal tech companies over-promising AI capabilities based on flawed benchmarks
AI development for legal assistance will require more diverse and representative datasets, directly impacting training methodologies.
Public trust in AI's capacity to deliver equitable access to legal services may be eroded if these benchmark issues are not addressed swiftly.
New regulatory frameworks for AI in legal applications could emerge, mandating specific testing standards to prevent deceptive performance claims.
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Read at arXiv cs.AI