LLMs in Scientific Research: Use Cases, Discipline Gaps, and Practical Insights

As large language models (LLMs) continue to evolve rapidly, the methodologies of scientific research are undergoing a digital transformation of unprecedented scale. A recent industry report from an AI research organization sheds light on how scientists are currently applying LLMs in their routine work. The report indicates that researchers primarily rely on LLMs for data analysis and academic writing, with notable variation in adoption rates across different scientific disciplines. These findings offer valuable guidance for the technical community and research institutions as they reassess the practical value of AI tools and chart future development directions.

Core Application Scenarios: Data Analysis and Academic Writing

According to the report, researchers’ high-frequency use of LLMs is concentrated in two fundamental areas: data processing and text generation.

1. Intelligent Assistance in Data Analysis

In the course of scientific research, data cleaning, transformation, and preliminary statistical analysis often consume a substantial portion of researchers’ time. Thanks to their robust code-generation capabilities, LLMs can effectively serve as “data science assistants.” Researchers can describe their requirements in natural language, and the model directly generates scripts in Python (e.g., using Pandas or NumPy) or R.

For example, when handling experimental data with missing values, a researcher could issue the following instruction and receive the corresponding processing code:

import pandas as pd
import numpy as np
# Assume df is the raw dataset loaded for scientific experimentation
# Fill missing values in a specific column with the median and generate a statistical summary
def process_research_data(df, target_column):
    if df[target_column].isnull().sum() > 0:
        median_val = df[target_column].median()
        df[target_column].fillna(median_val, inplace=True)
        print(f"Filled missing values using median {median_val}")
    summary = df.describe(include='all')
    return summary
# Execute data processing and summary extraction
# summary_stats = process_research_data(df, 'reaction_time')

This approach significantly lowers the barrier to routine data manipulation, allowing researchers to focus more on experimental design itself.

2. Academic Writing and Literature Review

In the context of academic writing, LLMs demonstrate strong capabilities in text structuring and polishing. Scientists use them for rapid extraction of literature abstracts, constructing paper outlines, cross-language translation and proofreading, and grammatical refinement of English-language manuscripts. For researchers whose first language is not English, LLMs help convey research findings with greater adherence to academic conventions, thereby improving the efficiency of publishing scientific results.

Disciplinary Differences: From Computer Science to Laboratory Physics

It is worth noting that the adoption of LLMs across the scientific community is far from uniform, exhibiting a clear long-tail distribution across fields.

1. High-Frequency Adoption: Computer Science, Mathematics, and Statistics

In these highly symbolic and digitized disciplines, the use of LLMs is particularly widespread. On one hand, daily research in computer science and statistics is closely tied to code and algorithmic logic—LLM-generated code can be executed and validated directly in local environments, creating a closed feedback loop. On the other hand, the literature and knowledge systems in these fields are highly accessible through open-source communities, meaning LLMs accumulate rich training data during pre-training and can deliver fairly accurate technical answers.

2. Lower-Frequency Adoption: Health Sciences and Laboratory Physics

In contrast, fields such as clinical medicine, biomedicine, and laboratory physics approach LLMs with more caution. This is primarily constrained by three factors:

• Data modality limitations: Laboratory physics often involves complex charts, instrument readings, and multi-modal unstructured data, which text-only models cannot directly process.
• Missing long-tail knowledge: Some highly specialized experimental physics or clinical pathology data are not publicly available, increasing the likelihood that models will produce hallucinations when handling niche terminology.
• Extremely low error tolerance: Health sciences involve patient safety and medical ethics—any factual error could have serious consequences, making researchers less reliant on AI-assisted outputs.

The Two Driving Forces Behind AI-Accelerated Scientific Discovery: Model Capability and Usage Strategy

The report emphasizes that AI holds the potential to accelerate scientific discovery, but the extent of this acceleration is not determined purely by the technology itself. Instead, it depends on the interaction between “the evolution of AI model capabilities” and “how scientists use them.”

1. Continuous Evolution of Model Capabilities

For LLMs to evolve from “writing assistants” into true “research collaborators,” models themselves need stronger long-context understanding, more sophisticated logical reasoning, and multi-modal parsing capabilities. In addition, through tool use and function calling, models can interact with external specialized computing software and database APIs, compensating for their limitations in precise arithmetic and computation.

2. User Strategy and Workflow Redesign

The strategies scientists adopt for AI applications are equally critical. Research teams with higher digital literacy are beginning to explore agentic architectures to build “automated research workflows.” For instance, through well-designed prompt engineering, LLMs can be positioned as domain-specific hypothesis generators or experimental result reviewers.

Practical Recommendations for Research Institutions: Cloud-Native and LLM Platforms

For research organizations seeking deep integration of AI technologies, building a secure and efficient intelligent research infrastructure is essential. By leveraging general-purpose LLM platforms from established cloud providers, research teams can conveniently call model APIs and combine them with container platforms for large-scale data processing.

1. Building a Private Research Knowledge Base with RAG

To address the persistent hallucination problems in fields like laboratory physics and health sciences, retrieval-augmented generation (RAG) is a recommended approach. Research teams can upload historical experimental records and internal literature to an object storage service, then extract and store feature vectors through a vector database. When a researcher raises a question, the system first retrieves relevant internal data, then passes it as context to the LLM, generating accurate answers grounded in the institution’s proprietary knowledge base.

2. Ensuring Data Security and Compliance in Research

Research data often contains sensitive information or unpublished intellectual property. When utilizing LLMs, institutions should adopt private deployment models or enterprise-grade cloud service interfaces to ensure end-to-end encryption during data exchange. Using security components provided by compliant cloud platforms enables fine-grained access control over research data, reducing the risk of core data leakage.

Conclusion

At present, the application of LLMs in scientific research is transitioning from early exploration to a more mature phase. While significant efficiency gains have already been demonstrated in data analysis and academic writing, crossing disciplinary boundaries and venturing into complex experimental scenarios still requires concerted efforts from both the technical and scientific communities. By developing a deeper understanding of AI capabilities and limitations, and by leveraging underlying compute infrastructure and LLM services from cloud providers, researchers can better harness these tools and take solid steps toward accelerating scientific discovery.