2024 SaulLM54BSaulLM141BScalingUpDom
- (Colombo et al., 2024b) ⇒ Pierre Colombo, Telmo Pires, Malik Boudiaf, Rui Melo, Dominic Culver, Sofia Morgado, Etienne Malaboeuf, Gabriel Hautreux, Johanne Charpentier, and Michael Desa. (2024). “SaulLM-54B & SaulLM-141B: Scaling Up Domain Adaptation for the Legal Domain.” doi:10.48550/arXiv.2407.19584
Subject Headings:
Notes
- It introduces SaulLM-54B and SaulLM-141B, two large language models specifically designed for the legal domain, based on the Mixtral architecture.
- It utilizes a comprehensive legal text corpus exceeding 500 billion tokens, sourced from various jurisdictions, to enhance the models' legal language comprehension and generation capabilities.
- It implements a three-phase approach for domain adaptation: continued pretraining, instruction fine-tuning, and legal preference alignment using domain-specific optimization.
- It demonstrates that the SaulLM models outperform previous open-source models and even larger general-purpose models like GPT-4 and Llama3 on legal benchmarks such as LegalBench-Instruct.
- It explores the benefits of continued pretraining, finding significant performance improvements in legal tasks when legal-specific data is incorporated.
- It addresses the challenges of instruction fine-tuning and alignment, showing that these processes are essential for optimizing the models' ability to interpret and execute legal commands accurately.
- It emphasizes the importance of scaling both model size and corpus size in domain adaptation, revealing that larger models with extensive legal pretraining data achieve superior performance.
Cited By
2024
Quotes
Abstract
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.
Introduction
- NOTE: Introduces SaulLM-54B and SaulLM-141B, large language models tailored for the legal sector, and outlines the research question regarding their domain adaptation.
Related Work
- NOTE: Discusses previous efforts and challenges in domain specialization for large language models, particularly in the legal domain.
Data Collection and Corpus Construction
- NOTE: Describes the assembly and refinement of a comprehensive legal text corpus tailored for training large language models.
Pretraining Corpora
- NOTE: Details the sources and preprocessing of the extensive legal text corpus used for continued pretraining.
Instruction Data
- NOTE: Explains the integration of general and domain-specific instructions to enhance the model’s ability to interpret and execute commands in legal scenarios.
Preference Data
- NOTE: Describes the incorporation of preference data from both general and legal-specific sources to improve model adaptability and precision.
Implementation Details & Evaluation Protocol
- NOTE: Provides technical details on the model selection, engineering, training process, and evaluation methods used to assess the performance of the models.
Experimental Results
- NOTE: Presents and analyzes the performance outcomes of the models, highlighting the benefits of continued pretraining and domain-specific tuning.
Global Results
- NOTE: Summarizes the overall performance of SaulLM models compared to existing models.
How much does continued pretraining help for the legal domain?
- NOTE: Examines the impact of continued pretraining on model performance in legal tasks.
How Much Does Legal Preference Alignment Help?
- NOTE: Investigates the effectiveness of legal preference alignment in improving model results.
Can We Achieve Further Improvements by Continuing Pretraining?
- NOTE: Analyzes the potential benefits of extending the pretraining process.
How Much Does Scaling Help?
- NOTE: Evaluates the impact of scaling the model size on performance across various legal tasks.
Energy Consumption
- NOTE: Reports on the energy consumption during the training of the models and discusses the efficiency of the process.
Conclusion & Limitations
- NOTE: Summarizes the key findings of the research and discusses the limitations of the current study.
Conclusion
- NOTE: Highlights the advancements and performance improvements achieved with SaulLM-54B and SaulLM-141B.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2024 SaulLM54BSaulLM141BScalingUpDom | Pierre Colombo Malik Boudiaf Dominic Culver Rui Melo Sofia Morgado Michael Desa Telmo Pires Etienne Malaboeuf Gabriel Hautreux Johanne Charpentier | SaulLM-54B & SaulLM-141B: Scaling Up Domain Adaptation for the Legal Domain | 10.48550/arXiv.2407.19584 | 2024 |