Galactica

Generates abstractive summaries of scientific papers and academic documents while preserving citation context and key findings. Uses transformer-based sequence-to-sequence architecture trained on scientific corpora to understand domain-specific terminology, methodologies, and research contributions. Extracts and ranks citations by relevance to enable literature review workflows.

Solves mathematical problems across algebra, calculus, statistics, and symbolic computation by generating step-by-step derivations and intermediate reasoning. Uses chain-of-thought prompting patterns combined with scientific notation understanding to decompose complex problems into solvable sub-steps. Integrates symbolic math libraries for verification of algebraic manipulations.

Generates structured Wikipedia-style articles on scientific topics by synthesizing knowledge from training data and organizing content into standard sections (introduction, methodology, results, references). Uses hierarchical content planning to determine section structure, then generates coherent prose for each section with appropriate technical depth. Integrates citation placeholders and cross-references.

Generates executable scientific code in Python, Julia, and MATLAB by understanding scientific libraries (NumPy, SciPy, PyTorch, TensorFlow) and domain-specific patterns. Produces code that implements algorithms, data processing pipelines, and numerical simulations with appropriate library calls and error handling. Integrates knowledge of scientific best practices like vectorization and numerical stability.

Analyzes molecular structures in SMILES or InChI notation to predict chemical properties, generate annotations, and identify functional groups. Uses graph neural network patterns learned during training to understand molecular topology and chemistry. Produces structured predictions of properties like solubility, toxicity, and reactivity alongside natural language explanations of chemical reasoning.

Analyzes protein sequences in FASTA format to predict functional domains, secondary structure, and biological function. Uses sequence alignment patterns and domain knowledge learned from scientific literature to identify conserved regions and functional motifs. Generates structured annotations mapping sequence positions to predicted functions and confidence scores.

Answers scientific questions across disciplines by retrieving relevant knowledge from training data and generating explanations with supporting reasoning. Uses retrieval-augmented patterns to identify relevant concepts and chains-of-thought to build multi-step answers. Produces answers with confidence indicators and caveats about knowledge limitations.

Generates scientific prose including abstracts, methods sections, and technical descriptions using domain-specific vocabulary and conventions learned from scientific literature. Uses controlled generation patterns to maintain technical accuracy and appropriate formality levels. Integrates citation formatting and scientific writing best practices.

Processes large collections of scientific documents or datasets to extract structured information including entities, relationships, and metadata. Uses information extraction patterns to identify scientific concepts, measurements, and experimental conditions. Produces structured outputs (JSON, CSV) suitable for downstream analysis and database ingestion.