3 accepted papers
Building robust and general reasoning ability is a central goal in the development of large language models (LLMs). Recent efforts increasingly turn to code as a rich training source, given its inherent logical structure and diverse reasoning paradigms—such as divide-and-conquer, topological orderin…
Regulatory agencies often operate with limited resources and rely on tips from the public to identify potential violations. However, processing these tips at scale presents significant operational challenges, as agencies must correctly identify and route relevant tips to the appropriate enforcement
The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform? To enable greater study of this question, we present LegalBench: a collaboratively constructed legal reasoning benchmark consisting of…