Back

Recent advancements in specialized LLMs have demonstrated their impressive performance and potential in various domains such as biology and medicine, education, legislation, computer science, and finance. In the field of biology and medicine, LLMs have been evaluated in scenarios like medical exams, question-answering on medical literature, clinical decision support, medical evidence summarization, and patient triaging. Evaluation methods include using real-world medical exams like USMLE and AIIMS/NEET, as well as assessing LLMs’ performance in application scenarios like PubMedQA and LiveQA. Human evaluations are also conducted to ensure safety and alignment with human values.

In the field of education, LLMs are evaluated from the perspectives of teaching and learning. LLMs as AI teachers are assessed on their pedagogical competence, speaking like a teacher, understanding a student, and helping a student. LLMs’ ability to assist with mathematics problems and provide essay feedback to students is compared to human tutors, and while positive learning gains are observed, human-created hints and feedback tend to outperform LLMs. Moreover, LLMs are explored as potential coaches for teachers to provide helpful feedback, but their generated feedback is often not novel or insightful.

LLMs also show promise in the field of legislation, with evaluations conducted on their exam ability in the legal domain, legal reasoning, and performance in real-world application scenarios. LLMs’ ability to pass the US legal Uniform Bar Examination and perform well on tasks like entailment and statutory reasoning is examined. Evaluations in application scenarios involve assessing LLMs’ performance in explaining legal terms, generating legal case judgment summaries, and ensuring factuality and clarity of responses. However, limitations are identified, such as inconsistencies in generated information and factuality.

In computer science, LLMs have extensive applications, particularly in code generation and programming assistance. Evaluations focus on the functional correctness of LLM-synthesized code and vulnerability detection in software. LLMs’ performance in code generation tasks and their assistance in code explanations, code writing, and collaborative software development are evaluated. LLM-generated code explanations are found to be easier to understand and have more accurate summaries compared to student-generated explanations.

In the domain of finance, LLMs are developed and evaluated to provide accurate and reliable answers related to financial knowledge. Applications of LLMs in finance include task formulation, synthetic data generation, and financial robo-advisory. LLMs’ performance in financial reasoning, financial literacy tests, and advice utilization tasks is assessed. The importance of continued research is highlighted to ensure ethical and responsible use of LLMs in finance.

Overall, specialized LLMs have shown significant progress and potential in various domains, but limitations and challenges still exist. Further research and improvements are needed to address gaps in performance, factuality, comprehension, reasoning, bias, and ethical considerations.

Words: 438