SaulLM-54B & SaulLM-141B: Scaling Up Domain Adaptation for the Legal
  Domain
Authors: name: Pierre Colombo, name: Telmo Pires, name: Malik Boudiaf, name: Rui Melo, name: Dominic Culver, name: Sofia Morgado, name: Etienne Malaboeuf, name: Gabriel Hautreux, name: Johanne Charpentier, name: Michael Desa

In this paper, we introduce SaulLM-54B and SaulLM-141B, two large language
models (LLMs) tailored for the legal sector. These models, which feature
architectures of 54 billion and 141 billion parameters, respectively, are based
on the Mixtral architecture. The development of SaulLM-54B and SaulLM-141B is
guided by large-scale domain adaptation, divided into three strategies: (1) the
exploitation of continued pretraining involving a base corpus that includes
over 540 billion of legal tokens, (2) the implementation of a specialized legal
instruction-following protocol, and (3) the alignment of model outputs with
human preferences in legal interpretations. The integration of synthetically
generated data in the second and third steps enhances the models' capabilities
in interpreting and processing legal texts, effectively reaching
state-of-the-art performance and outperforming previous open-source models on
LegalBench-Instruct. This work explores the trade-offs involved in
domain-specific adaptation at this scale, offering insights that may inform
future studies on domain adaptation using strong decoder models. Building upon
SaulLM-7B, this study refines the approach to produce an LLM better equipped
for legal tasks. We are releasing base, instruct, and aligned versions on top
of SaulLM-54B and SaulLM-141B under the MIT License to facilitate reuse and
collaborative research.