Sci-LLMs: Scientific Language Models

• Scientific Large Language Models (Sci-LLMs) are advanced transformer-based models that comprehend and generate complex scientific language from textual, symbolic, and multimodal data.
• They integrate domain-specific pretraining, targeted fine-tuning, and external tool augmentation to enhance hypothesis generation, autonomous experimentation, and knowledge synthesis.
• Deployment challenges include data heterogeneity, hallucination risks, and safety alignment, driving continuous innovations in evaluation, governance, and robust multi-modal integration.

Scientific LLMs (Sci-LLMs) are advanced transformer-based AI systems specially adapted for understanding, generating, and interacting with scientific knowledge across a range of modalities, domains, and workflows. Combining large-scale pretraining with domain-specific data, targeted fine-tuning, and modular integration with external tools, Sci-LLMs are rapidly transforming research practices in natural science, engineering, and interdisciplinary fields. Their deployment is accelerating hypothesis generation, autonomous experimentation, knowledge synthesis, and domain-specific reasoning—while simultaneously raising unique challenges in data curation, interpretability, evaluation, and safety.

1. Definition, Scope, and Conceptual Framework

Sci-LLMs are LLMs that are either designed or adapted to operate in scientific domains, learning from both conventional (textual) and specialized (symbolic, structured, or multimodal) scientific representations (Zhang et al., 26 Jan 2024). These models extend beyond general linguistic comprehension to handle “scientific language,” including:

• Textual scientific language: Research articles, reviews, experimental protocols, and patents, written with extensive domain-specific jargon and structure.
• Scientific symbolic languages: Encodings such as SMILES, SELFIES, and InChI (chemistry); amino acid and nucleotide sequences (biology, genomics); mathematical and formal proof languages; and notation for physics and engineering (Zhang et al., 26 Jan 2024, Hu et al., 28 Aug 2025).
• Multimodal/structured data: Knowledge graphs (e.g., for molecular or protein interactions), tables, code, figures, and even imagery (microscopy, geoscience, medical) (Yu et al., 21 May 2025, Hu et al., 28 Aug 2025).

Scientific knowledge is inherently hierarchical: facts and observations (lowest level) support theories and models, which combine into methodological workflows, culminating in high-level insights and discovery (Hu et al., 28 Aug 2025). Sci-LLMs are tasked with reasoning and acting across all these layers.

2. Model Architectures, Training Paradigms, and Data Foundations

Most Sci-LLMs are built upon one of three scalable transformer model classes (Zhang et al., 16 Jun 2024, Zhang et al., 26 Jan 2024):

• Encoder-only models (e.g., BERT, SciBERT, ChemBERT, BioBERT): Well-suited for scientific document understanding, retrieval, and classification.
• Decoder-only models (e.g., GPT, Galactica, Mistral variants): Used for science-focused text generation, hypothesis exploration, code synthesis, and experiment planning.
• Encoder-decoder (seq2seq) models (e.g., BART, T5, MolT5): Employed for cross-modal and complex mapping tasks such as table-to-text, molecule captioning, or translation between symbolic and natural language.

Modern Sci-LLMs incorporate advanced modalities and tool integrations:

• Domain-specific tokenization and embedding: To parse SMILES, sequences, or tables (Zhang et al., 26 Jan 2024, Hu et al., 28 Aug 2025).
• Architecture augmentation: Graph neural networks for molecular structures, vision encoders for figures, specialized attention mechanisms for long sequences.
• Retrieval-augmented generation pipelines: Incorporation of up-to-date literature, experimental protocols, or structured knowledge graphs to supplement parametric model knowledge and support verifiability (Xiong et al., 4 Nov 2024, Zheng et al., 2023).

Data sourcing is central. Pretraining draws from:

• Scientific literature (PubMed, PMC, arXiv, Semantic Scholar, patent corpora).
• Domain-specific databases (ZINC, PubChem, UniProt, PDB, ChEMBL, Materials Project).
• Tables, knowledge graphs, and curated datasets (e.g., CHEBI-20-MM includes molecular images, SMILES, IUPAC, and graph data) (Liu et al., 6 Feb 2024).
• Multi-modal corpora for complex, cross-field problems (Yu et al., 21 May 2025, Hu et al., 28 Aug 2025). Automated and semi-automated annotation pipelines, including active learning and self-reflective data augmentation, are employed to compensate for chronic domain labeling shortages (Zhang et al., 15 Jan 2024, Lin et al., 2023, Hu et al., 28 Aug 2025).

3. Scientific Reasoning, Tools, and Autonomous Agency

Emergent Sci-LLMs are not mere text generators. State-of-the-art systems (sometimes termed “Intelligent Agents”) couple multiple LLMs with modular tool interfaces for true autonomous experimentation (Boiko et al., 2023, Xiong et al., 4 Nov 2024, Hu et al., 28 Aug 2025):

• Atomic modules: Web search, documentation retrieval via vector embeddings, code synthesis/execution sandboxes, and laboratory automation (hardware control, cloud labs).
• Planner-Controller orchestrations: Modular “Planners” receive user prompts (e.g., “execute Suzuki couplings”) and iteratively decompose them into actionable workflows—issuing commands for search, code, documentation, or experiment modules and correcting errors as detected.
• Scientific synthesis and workflow execution: Example workflows include searching for reaction conditions, generating experimental code, running robotic lab protocols, and analyzing downstream data (e.g., UV-Vis, GC-MS) (Boiko et al., 2023).
• Integration with knowledge graphs and external databases: Enhances hypothesis generation accuracy, explicit reasoning over relational chains, and hallucination detection (e.g., the KG-CoI system computes explicit chains-of-ideas and calculates stepwise support within scientific KGs) (Xiong et al., 4 Nov 2024).

4. Capabilities, Benchmarks, and Evaluation

Sci-LLMs are systematically evaluated on both narrow and broad scientific tasks:

• Natural language QA, summarization, and information retrieval: Assessed via benchmarks such as C-Eval, MolQA, PubMedQA, MatBench, and arXiv-based question banks (Feng et al., 13 Jun 2024, Yu et al., 21 May 2025, Zhang et al., 26 Jan 2024).
• Specialized tasks/representations: Sequence modeling (DNA, protein), symbolic manipulation (chemistry, mathematics), molecular property prediction, structure/function inference (Zhang et al., 26 Jan 2024, Liu et al., 6 Feb 2024).
• Multimodal integration: Table-based reasoning (ProtTab, MolTab), knowledge graph querying (GoKG, HipKG), visual data (X-ray, MR, satellite) (Yu et al., 21 May 2025, Hu et al., 28 Aug 2025).
• Process-oriented and agentic tasks: Multi-step experiment design, multi-modal synthesis, on-the-fly literature synthesis, and tool usage (workflow planning, code execution, experimental automation) (Boiko et al., 2023, Hu et al., 28 Aug 2025). Scientific evaluation is shifting from static questions toward dynamic, process-oriented, and cross-modal benchmarks—measuring not only factual recall but chain-of-thought quality, safety alignment (as in SciSafeEval), and practical laboratory or design ability (Li et al., 2 Oct 2024, Yu et al., 21 May 2025, Giglou et al., 27 Sep 2024, Feng et al., 13 Jun 2024).

5. Challenges, Safety Alignment, and Limitations

Deployment of Sci-LLMs presents unique challenges:

• Data scarcity, heterogeneity, and domain adaptation: Scientific data is more fragmented, multidimensional, and label-scarce than general NLP data. Cross-modality (text, table, image, graph) and cross-scale (nano to planetary) complexities impose substantial adaptation burdens (Zhang et al., 26 Jan 2024, Hu et al., 28 Aug 2025, Liu et al., 6 Feb 2024).
• Hallucination and Reliability: LLMs can generate plausible-sounding but incorrect (“hallucinated”) conclusions, a severe risk in scientific settings. Approaches for mitigation include retrieval-augmented generation, explicit chain-of-ideas prompting, automated verification against KGs, and stepwise confidence scoring (Xiong et al., 4 Nov 2024, Zhang et al., 22 May 2025, Zheng et al., 2023, Yu et al., 21 May 2025).
• Guardrailing: Scientific outputs demand time-sensitivity (up-to-date evidence), context awareness (disciplinary and methodological variations), explicit attribution and IP protection, and the ability to handle contradictory findings. Guardrails must be layered across trustworthiness, bias, safety, and legal compliance through white-box, black-box, and gray-box approaches (e.g., formal verification, output filtering, retrieval-augmented reasoning, human-in-the-loop) (Pantha et al., 12 Nov 2024).
• Safety and dual-use: SciSafeEval and related benchmarks highlight the risks of LLMs being subverted to produce hazardous instructions (e.g., for toxins or bioweapons) and test their resilience to adversarial prompts (“jailbreaks”) (Li et al., 2 Oct 2024, Boiko et al., 2023, Feng et al., 13 Jun 2024).
• Metric Gaps and Evaluation Process: Existing benchmarks underrepresent process, reasoning, absence-detection, and cross-modal integration (Yu et al., 21 May 2025), often overstating model capabilities on factual recall while missing context sensitivity, multi-source reasoning, and information-absence detection.

6. Emerging Trends and Future Directions

Sci-LLMs are evolving along several axes:

• Unified, continually updating data ecosystems: Automated curation, annotation, and provenance management for multimodal, hierarchical scientific corpora, including negative and unpublished findings, to reduce data latency and increase breadth (Hu et al., 28 Aug 2025).
• Process- and agent-based systems: Progress toward autonomous, tool-integrated agents with capabilities for scientific planning, experimentation, self-correction, and living knowledge base updating (Boiko et al., 2023, Hu et al., 28 Aug 2025).
• Parameter- and data-efficient approaches: Parameter-efficient fine-tuning (LoRA, QLoRA, Adapter Tuning), active learning, and continual learning to reduce compute costs and catastrophic forgetting, enabling adaptation to new scientific advances (To et al., 20 Aug 2024).
• Formal verification and explainability: Progress on model transparency, step-by-step explainability (with human- or AI-generated explanations), formal logic checking, and consistency validation (Zheng et al., 2023, Pantha et al., 12 Nov 2024).
• Collaborative agent frameworks: Multi-agent systems for synthesis, critique, consensus formation, and automated experimental cycles—the model as both planner and self-referee (Hu et al., 28 Aug 2025).
• Ethics and governance: Ongoing work to define domain-specific ethical, legal, and regulatory frameworks for deployment (including attribution, privacy, and dual-use mitigation) (Pantha et al., 12 Nov 2024, Boiko et al., 2023).

Table: Representative Benchmark Classes in Sci-LLMs

Benchmark	Modality	Core Competency
SciCUEval	Text, table, KG	Contextual reasoning, absence detection, multi-source integration (Yu et al., 21 May 2025)
SciSafeEval	Text, molecule, seq	Safety/harmlessness, jailbreak resistance (Li et al., 2 Oct 2024)
SciKnowEval	Text	Multi-level knowledge: recall, reasoning, application (Feng et al., 13 Jun 2024)

These advances collectively situate Sci-LLMs as more than generative models: they are evolving scientific agents capable of robust cross-modal reasoning, workflow execution, self-correction, and integration into full-cycle research practice. Ongoing challenges in data standardization, evaluation, and safety alignment remain critical areas for the community.