Selecting embedding models for academic or scientific literature requires balancing domain specificity, model architecture, and practical constraints. Academic texts often contain specialized terminology, complex concepts, and dense arguments, so models must capture nuanced semantic relationships. Start by evaluating whether a general-purpose model (like BERT or Word2Vec) suffices or if a domain-specific alternative (like SciBERT or SPECTER) is better. Domain-specific models are pre-trained on scientific corpora, which improves their ability to handle jargon and context. For example, SPECTER, trained on citation graphs, excels at understanding how papers relate through references, making it useful for tasks like literature recommendation. However, general models might still perform well if your application doesn’t require deep domain expertise, especially when fine-tuned on your dataset.
Next, consider the model’s ability to handle long-form text. Academic papers are lengthy, so models optimized for sentence-level embeddings (e.g., Sentence-BERT) may struggle with full documents. Document-level models like Longformer or models with hierarchical architectures (e.g., splitting text into sections and aggregating embeddings) are better suited. For example, the Longformer’s attention mechanism scales efficiently with longer sequences, making it practical for processing entire research papers. Additionally, multilingual support matters if your corpus includes non-English literature. Models like XLM-R or multilingual BERT variants can handle multiple languages but may sacrifice performance in English-specific tasks compared to monolingual models.
Finally, prioritize practical factors like computational resources and ease of integration. Large models like GPT-3 or PaLM offer strong performance but require significant infrastructure for inference and fine-tuning. For resource-constrained environments, smaller models like MiniLM or DistilBERT provide a balance between efficiency and accuracy. Open-source models (e.g., those on Hugging Face Hub) allow customization, while proprietary APIs (like OpenAI’s embeddings) offer convenience but less control. Test multiple models on a subset of your data using task-specific metrics—for example, measuring retrieval accuracy with cosine similarity or clustering quality with silhouette scores. If metadata (e.g., citations, keywords) is available, consider hybrid approaches that combine embeddings with metadata-based features for improved results.