Specialized language models
Domain-specific language models can outperform more generalized foundational models
A research group recently released a paper, titled SaulLM-7B: A pioneering Large Language Model for Law. The paper discusses SaulLM-7B, a 7 billion parameter language model specifically designed and trained for the legal domain. The paper claims that it is the first large language model publicly released that is tailored for understanding and generating legal text.
Some key points about the model:
SaulLM-7B is built by pretraining the open-source 7B parameter Mistral model on a large body of over 30 billion tokens of legal data from various jurisdictions like the US, UK, Canada, Europe, etc.
It then goes through an instruction fine-tuning stage using a mixture of general instructional data as well as synthetically generated legal instruction data.
This two-stage process of legal pretraining followed by legal instruction tuning allows SaulLM-7B to exhibit state-of-the-art capabilities on various legal benchmarks like LegalBench and the legal tasks in the MMLU benchmark.
The authors also introduce a tool, called LegalBench-Instruct, an enhanced version of LegalBench designed to better evaluate legal reasoning abilities of language models.
SaulLM-7B and its instruction tuned variant SaulLM-7B-Instruct are released open source under the MIT license to foster further research in AI for the legal domain.
How could lawyers use this tool?
There are several potential ways that lawyers could use a large language model like SaulLM-7B:
Legal research and analysis
The model could be used to quickly analyze and summarize large volumes of legal documents, cases, contracts, etc., to aid in legal research and preparation.
It could identify relevant precedents, laws, and arguments related to a given legal issue or case.
Contract review and drafting
SaulLM-7B could review and provide insights on contract language, highlight potential issues or areas of ambiguity.
It could potentially assist in drafting new contracts by generating language adhering to legal requirements.
Legal writing and document generation
The model could aid in drafting legal briefs, memos, pleadings and other documents by generating initial drafts or assisting with language and phrasing.
Legal question answering
Lawyers could use it as a knowledgeable assistant to quickly get information and answers related to legal concepts, procedures, cases, etc.
Legal task automation
Certain repetitive tasks like document review, due diligence, discovery, etc., could potentially be automated or augmented using the model’s capabilities.
How can these observations be generalized to the concept of specialized large language models?
There are parallels we can draw between SaulLM-7B for the legal industry, and the development of specialized large language models for other specific domains. The key idea is to take a powerful general-purpose foundation model and adapt it through further training on domain-specifc data to imbue it with specialized knowledge and capabilities.
Some examples of how organizations across different industries could use specialized language models:
Finance/Banking: A model trained on financial data like reports, prospectuses, regulations, etc., could assist in document analysis, risk assessment, customer service inquiries, etc.
Healthcare/Biomedical: Models trained on medical literature, clinical notes, drug data, etc., could aid doctors and researchers in quickly retrieving relevant information, analysis of health care, exploring treatment options, etc.
Scientific research: Domain models for fields like physics, chemistry, climate science trained on research papers and data could accelerate literature review, concept exploration, experiment design, etc.
Enterprise IT/software: Models focused on coding, developer documentation and knowledge bases could provide intelligent code autocompletion, documentation assistance, answer programmer questions, etc.
Manufacturing/Engineering: Models ingesting technical specs, product designs, safety codes, etc., could support design reviews, troubleshooting, and requirements analysis among engineering teams.
The key benefits that specialized language models unlock are:
Improved accuracy and relevance of outputs by leveraging domain knowledge.
Productivity gains by automating highly manual information retrieval and knowledge work.
Enabling easier access to specialized domain knowledge for non-experts.
Augmenting domain experts’ capabilities through intelligent analysis and querying.
In order for specialized language models to be useful, careful data sourcing, curation, and monitoring for biases is required. The potential to deliver operational efficiencies is immense.