SaulLM-7B: A pioneering Large Language Model for Law

In this paper, we introduce SaulLM-7B, a large language model (LLM) tailored for the legal domain. With 7 billion parameters, SaulLM-7B is the first LLM designed explicitly for legal text comprehension and generation. Leveraging the Mistral 7B architecture as its foundation, SaulLM-7B is trained on an English legal corpus of over 30 billion tokens. SaulLM-7B exhibits state-of-the-art proficiency in understanding and processing legal documents. Additionally, we present a novel instructional fine-tuning method that leverages legal datasets to further enhance SaulLM-7B's performance in legal tasks. SaulLM-7B is released under the MIT License.

Video-based computer aided arthroscopy for patient specific reconstruction of the Anterior Cruciate Ligament

The Anterior Cruciate Ligament (ACL) tear is a common medical condition that is treated using arthroscopy by pulling a tissue graft through a tunnel opened with a drill. The correct anatomical position and orientation of this tunnel is crucial for knee stability, and drilling an adequate bone tunnel is the most technically challenging part of the procedure. This paper presents, for the first time, a guidance system based solely on intra-operative video for guiding the drilling of the tunnel. Our solution uses small, easily recognizable visual markers that are attached to the bone and tools for estimating their relative pose. A recent registration algorithm is employed for aligning a pre-operative image of the patient's anatomy with a set of contours reconstructed by touching the bone surface with an instrumented tool. Experimental validation using ex-vivo data shows that the method enables the accurate registration of the pre-operative model with the bone, providing useful information for guiding the surgeon during the medical procedure.