An Introduction to Large Language Models for Scientific Discovery

Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, revolutionizing natural language processing and extending their influence to scientific research. This survey provides an accessible introduction to LLMs and their growing applications in scientific discovery. We cover fundamental concepts of LLMs, including their architectures (e.g., Transformers), training methodologies (e.g., pre-training, fine-tuning), and key functionalities relevant to scientific tasks (e.g., text generation, summarization, question answering). We then delve into a comprehensive overview of how LLMs are being leveraged across different scientific disciplines, such as materials science, chemistry, biology, and physics. Specific applications include hypothesis generation, experimental design, data analysis, scientific literature review, and automated code generation for simulations. Finally, we discuss current challenges and future directions, emphasizing ethical considerations, interpretability, and the integration of LLMs with traditional scientific methods.

Scientific data is growing exponentially, making it impossible for humans to read every paper or analyze every molecule. While LLMs have shown success in general domains, there is a knowledge gap in how to effectively apply them to specialized scientific problems due to unique challenges like complex terminology, multimodal data (graphs, equations), and the high cost of errors.

The paper adopts a tutorial and systematic review methodology. It begins by establishing the technical foundations of LLMs (Transformers, Scaling Laws). It then provides a taxonomy of scientific tasks suitable for LLMs. The authors synthesize findings from various studies to illustrate the 'lifecycle' of building a scientific LLM: Data Collection -> Model Architecture -> Training -> Evaluation. No new experimental data is presented; rather, it organizes existing knowledge.

The paper concludes that LLMs act as effective 'polymaths' in science. Key takeaways include: 1) LLMs can accelerate literature reviews and knowledge extraction. 2) They show promise in generative tasks like protein design and molecule generation when treated as language problems. 3) General-purpose models often fail at specialized scientific reasoning without fine-tuning or RAG. 4) The 'human-in-the-loop' approach is currently the safest and most effective way to deploy these models.

This paper is a vital primer for a rapidly evolving field. It successfully demystifies complex AI concepts for domain scientists. However, its 'snapshot' nature means it misses very recent advancements like Mixture-of-Experts (MoE) efficiency or the latest long-context models (Gemini 1.5). It relies heavily on the promise of LLMs while glossing over the significant energy costs and the 'reproducibility crisis' potentially worsened by non-deterministic model outputs. The distinction between 'memorization' and true 'reasoning' in scientific contexts could be more critically examined.