# SaulLM-54B & SaulLM-141B: Scaling Up Domain Adaptation for the Legal Domain

arXiv: https://arxiv.org/abs/2407.19584 · HTML: https://mlsanity.com/paper/2407.19584/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 64 of its 88 references that are in the map.
- First posted on arXiv 28 Jul 2024.

## Builds on (most cited first, 30 of 64)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models](https://mlsanity.com/paper/2401.06066/) (2024)
- [Enhancing Chat Language Models by Scaling High-quality Instructional Conversations](https://mlsanity.com/paper/2305.14233/) (2023)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://mlsanity.com/paper/2301.13688/) (2023)
- [MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies](https://mlsanity.com/paper/2404.06395/) (2024)
- [Mixture-of-Experts with Expert Choice Routing](https://mlsanity.com/paper/2202.09368/) (2022)
- [TinyLlama: An Open-Source Small Language Model](https://mlsanity.com/paper/2401.02385/) (2024)
- [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://mlsanity.com/paper/2306.04751/) (2023)
- [Scaling Data-Constrained Language Models](https://mlsanity.com/paper/2305.16264/) (2023)
- [MoE-LLaVA: Mixture of Experts for Large Vision-Language Models](https://mlsanity.com/paper/2401.15947/) (2024)

## Built on by (most cited first, 30 of 39)

- [Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report](https://mlsanity.com/paper/2504.21039/) (2025)
- [Demystifying Domain-adaptive Post-training for Financial LLMs](https://mlsanity.com/paper/2501.04961/) (2025)
- [Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual Settings](https://mlsanity.com/paper/2503.15620/) (2025)
- [Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report](https://mlsanity.com/paper/2508.01059/) (2025)
- [PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice](https://mlsanity.com/paper/2601.16669/) (2026)
- [Large Language Models Meet Legal Artificial Intelligence: A Survey](https://mlsanity.com/paper/2509.09969/) (2025)
- [Domain Adaptation of Foundation LLMs for e-Commerce](https://mlsanity.com/paper/2501.09706/) (2025)
- [Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models](https://mlsanity.com/paper/2507.02870/) (2025)
- [Fine-Tune an SLM or Prompt an LLM? The Case of Generating Low-Code Workflows](https://mlsanity.com/paper/2505.24189/) (2025)
- [AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance](https://mlsanity.com/paper/2510.01474/) (2025)
- [Large Language Models in Politics and Democracy: A Comprehensive Survey](https://mlsanity.com/paper/2412.04498/) (2024)
- [Standardizing Intelligence: Aligning Generative AI for Regulatory and Operational Compliance](https://mlsanity.com/paper/2503.04736/) (2025)
- [LAR-ECHR: A New Legal Argument Reasoning Task and Dataset for Cases of the European Court of Human Rights](https://mlsanity.com/paper/2410.13352/) (2024)
- [Legal Evalutions and Challenges of Large Language Models](https://mlsanity.com/paper/2411.10137/) (2024)
- [Continual Pre-Training is (not) What You Need in Domain Adaption](https://mlsanity.com/paper/2504.13603/) (2025)
- [Bye-bye, Bluebook? Automating Legal Drudgery With AI-Augmented Rule Following](https://mlsanity.com/paper/2505.02763/) (2025)
- [Response Uncertainty and Probe Modeling: Two Sides of the Same Coin in LLM Interpretability?](https://mlsanity.com/paper/2505.18575/) (2025)
- [LegalCiteBench: Evaluating Citation Reliability in Legal Language Models](https://mlsanity.com/paper/2605.10186/) (2026)
- [LegalBench.PT: A Benchmark for Portuguese Law](https://mlsanity.com/paper/2502.16357/) (2025)
- [JurisTCU: A Brazilian Portuguese Information Retrieval Dataset with Query Relevance Judgments](https://mlsanity.com/paper/2503.08379/) (2025)
- [FineScope : SAE-guided Data Selection Enables Domain Specific LLM Pruning and Finetuning](https://mlsanity.com/paper/2505.00624/) (2025)
- [LexRubric: A Rubric-Guided Diagnostic Benchmark for Open-Ended Legal Tasks](https://mlsanity.com/paper/2606.09389/) (2026)
- [Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study](https://mlsanity.com/paper/2412.06272/) (2024)
- [The interplay between domain specialization and model size](https://mlsanity.com/paper/2501.02068/) (2025)
- [PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements](https://mlsanity.com/paper/2506.00608/) (2025)
- [SteuerLLM: Local specialized large language model for German tax law analysis](https://mlsanity.com/paper/2602.11081/) (2026)
- [Where Experts Disagree, Models Fail: Detecting Implicit Legal Citations in French Court Decisions](https://mlsanity.com/paper/2603.22973/) (2026)
- [NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models](https://mlsanity.com/paper/2606.15646/) (2026)
- [LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents](https://mlsanity.com/paper/2509.02241/) (2025)
- [Microsaccade-Inspired Probing: Positional Encoding Perturbations Reveal LLM Misbehaviours](https://mlsanity.com/paper/2510.01288/) (2025)

## Abstract (arXiv)

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.