Large Language Models for Critical Domains: Transforming Finance, Healthcare, and Law

Core Concepts

Large language models are revolutionizing finance, healthcare, and law by enhancing diagnostic and treatment methodologies, innovating financial analytics, and refining legal interpretation and compliance strategies, while posing ethical challenges that require transparent, fair, and robust AI systems.

Abstract

This survey explores the methodologies, applications, challenges, and forward-looking opportunities of large language models (LLMs) within the critical societal domains of finance, healthcare, and law. These domains are characterized by their reliance on professional expertise, highly confidential data, extensive multimodal documents, high legal risk and strict regulations, and the requirement for explainability and fairness. In the finance domain, the survey discusses tasks and datasets in financial NLP, including sentiment analysis, information extraction, question answering, and text-enhanced stock movement prediction. It examines various financial LLMs, their performance evaluations, and LLM-based methodologies for financial tasks and challenges. The survey highlights the instrumental role of LLMs in innovating financial analytics and the need for further research to improve complex reasoning abilities. In the healthcare domain, the survey covers tasks and benchmarks for medical NLP, the application of LLMs for medicine and healthcare, and novel tasks such as abnormality and ambiguity detection, medical report generation, medical free-form instruction evaluation, and medical-imaging classification via natural language. It emphasizes the potential of LLMs in enhancing diagnostic and treatment methodologies while addressing the challenges of data confidentiality and multimodal integration. In the legal domain, the survey examines tasks and datasets in legal NLP, legal-specific LLMs, and LLM-based methodologies for legal tasks and challenges. It underscores the importance of LLMs in refining legal interpretation and compliance strategies, while highlighting the need for transparent reasoning and bias mitigation to maintain public trust and regulatory compliance. The survey also discusses the ethical principles and considerations for adopting LLMs in these critical domains, including ethical principles, ethical considerations, and domain-specific ethics. It emphasizes the imperative for interdisciplinary cooperation, methodological advancements, and ethical vigilance to maximize the benefits of LLMs while mitigating their risks in these precision-dependent sectors.

Stats

"These domains are major cornerstones of societal function and well-being, each playing a critical role in the fabric of daily life and the broader economic and social systems." "The finance domain involves complex financial analysis, investment strategies, and economic forecasting, necessitating deep knowledge of financial theories, market behavior, and fiscal policy." "Healthcare requires specialized knowledge in medical sciences, patient care, diagnostics, and treatment planning, and professionals are trained for years in their specific fields." "The legal domain demands a thorough understanding of legal principles, statutes, case law, and judicial procedures, with practitioners spending extensive periods in legal education and training."

Quotes

"The advent of large language models (LLMs) such as ChatGPT (OpenAI, 2022) and GPT-4 (OpenAI, 2023) marks a significant milestone in the evolution of artificial intelligence." "Reliance on Professional Expertise, Highly Confidential Data, Extensive Multimodal Documents, High Legal Risk and Strict Regulations, and the Requirement for Explainability and Fairness are the key characteristics of the finance, healthcare, and law domains." "Developing LLMs that can accurately interpret and correlate information across modalities is crucial, demanding innovative approaches to model architecture and data processing."

Key Insights Distilled From

A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law

by Zhiyu Zoey C... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.01769.pdf

Deeper Inquiries

How can LLMs be further improved to handle the complex reasoning and multimodal integration required in high-stakes domains like finance, healthcare, and law?

In order to enhance LLMs for handling the intricate reasoning and multimodal integration necessary in critical domains such as finance, healthcare, and law, several strategies can be implemented: Specialized Pre-training: Develop domain-specific pre-training datasets that incorporate a wide range of textual, numerical, and visual data relevant to each sector. This will help LLMs understand the intricacies of the domain-specific information. Multimodal Architectures: Design LLM architectures that can effectively process and integrate information from various modalities such as text, images, and tables. This will enable LLMs to comprehend and analyze diverse data types commonly found in these sectors. Fine-tuning with Instruction: Implement instruction fine-tuning techniques where LLMs are trained to follow specific guidelines or instructions related to complex reasoning tasks. This can help improve their ability to perform intricate reasoning processes. Knowledge Graph Integration: Incorporate knowledge graphs into LLMs to enhance their understanding of relationships between entities and concepts in the domain. This can facilitate more nuanced reasoning and decision-making. Ethical and Regulatory Compliance: Ensure that LLMs are trained and fine-tuned with ethical considerations and regulatory guidelines specific to each domain. This will help in developing responsible AI systems that adhere to legal and ethical standards. By implementing these strategies, LLMs can be further optimized to handle the complex reasoning and multimodal integration required in high-stakes domains like finance, healthcare, and law.

What are the potential risks and unintended consequences of deploying LLMs in these critical domains, and how can they be effectively mitigated?

Deploying LLMs in critical domains like finance, healthcare, and law comes with inherent risks and unintended consequences, including: Bias and Fairness: LLMs may perpetuate biases present in the training data, leading to unfair outcomes. Mitigation involves thorough data preprocessing, bias detection algorithms, and diverse training data to ensure fairness. Data Privacy and Security: LLMs may inadvertently expose sensitive information, posing privacy risks. Robust data encryption, access controls, and anonymization techniques can mitigate these risks. Interpretability and Explainability: LLMs' complex decision-making processes may lack transparency, making it challenging to understand their reasoning. Techniques like attention mechanisms and model-agnostic interpretability methods can enhance explainability. Regulatory Compliance: LLMs must comply with sector-specific regulations, and non-compliance can lead to legal repercussions. Regular audits, compliance checks, and transparency in model development can help mitigate regulatory risks. Model Performance Degradation: Over-reliance on LLMs without human oversight can lead to performance degradation over time. Continuous monitoring, feedback loops, and human-in-the-loop systems can prevent such issues. Mitigating these risks involves a combination of technical solutions, regulatory frameworks, and ethical guidelines to ensure the responsible deployment of LLMs in critical domains.

How can the interdisciplinary collaboration between AI researchers, domain experts, and regulatory bodies be fostered to ensure the responsible and ethical development of LLMs for these precision-dependent sectors?

To foster interdisciplinary collaboration for the responsible and ethical development of LLMs in precision-dependent sectors, the following strategies can be implemented: Establish Collaborative Platforms: Create forums, workshops, and conferences where AI researchers, domain experts, and regulatory bodies can interact, share insights, and collaborate on LLM development. Ethics and Compliance Training: Provide training programs on ethics, compliance, and regulatory requirements for AI researchers and domain experts to ensure a common understanding of responsible AI development. Joint Research Projects: Encourage joint research projects that involve AI researchers, domain experts, and regulatory bodies working together to address specific challenges and develop solutions that meet regulatory standards. Transparency and Communication: Foster open communication channels between all stakeholders to discuss concerns, share progress, and address ethical considerations in LLM development. Regulatory Consultations: Involve regulatory bodies in the development process from the early stages to ensure that LLMs comply with sector-specific regulations and ethical guidelines. By promoting collaboration, transparency, and communication among AI researchers, domain experts, and regulatory bodies, the responsible and ethical development of LLMs for precision-dependent sectors can be ensured.