From Fragmented Literature to Reusable Knowledge: Concept-Conditioned Information Integration through LLMs

Abstract

Recent scientific language models achieve strong performance by scaling model size and training on large domain-specific corpora; however, they primarily rely on raw textual data and lack explicit mechanisms for knowledge abstraction and reuse. In this paper, we present StemForce-LM, a framework for transforming unstructured scientific literature into structured, reusable supervision for language model training. The proposed approach generates concept-conditioned question–answer pairs that decouple underlying principles from document-specific context, enabling cross-document knowledge integration. The framework consists of four stages: domain corpus curation via citation graph expansion and prompt filtering, concept-conditioned knowledge extraction through constrained prompting, information integration via Q/A-based fine-tuning, and evaluation using debiased pairwise comparisons. We evaluate the framework on a stem cell mechanobiology testbed (StemForce-LM) and demonstrate improved domain-specific reasoning accuracy, strong performance against substantially larger models. These results indicate that structured knowledge extraction provides an effective and scalable alternative to raw data scaling for advancing scientific language models.