aiqbench analysis
This study demonstrates that large language models can reliably extract coherent, physically interpretable structures from vast, heterogeneous scientific literature, even in domains characterized by competing interpretations. By constructing a knowledge graph from nearly 18,000 publications on high-temperature superconductivity, the research confirms that LLM representations remain robust across different prompting and decoding strategies. For the tech community, this validates LLMs as scalable tools for deciphering latent scientific consensus where traditional information extraction methods may struggle with implicit or evolving claims.
Can LLMs extract scientific consensus? A case study in high-temperature superconductivity
arXiv:2606.07570v1 Announce Type: cross Abstract: Scientific knowledge is increasingly dispersed across vast and heterogeneous scientific literature, where important claims are often implicit, evolving, and internally debated. While large language models (LLMs) have shown impressive performance in information extraction and summarization, their ability to recover latent scientific consensus remains unclear. Here, we investigate this problem in the context of high-temperature superconductivity (HTS), a long-standing and highly debated topic in condensed matter physics, as a challenging testbed. Using near 18,000 highly-cited publications over the past seven decades, we construct a structured knowledge graph linking competing superconducting mechanisms, material families, evidential modalities, and citation relations. We find that LLM-extracted representations recover coherent and physically interpretable structures, including family-dependent mechanism profiles, evidence-specific correlations, and citation-mediated temporal evolution of scientific beliefs. Ablation studies on LLM further show that the global structure remains robust across prompting, decoding, and model variations. Our results suggest that LLMs can indeed serve as scalable tools for deciphering scientific knowledge in domains characterized by competing interpretations and evolving knowledge.
Abstract:Scientific knowledge is increasingly dispersed across vast and heterogeneous scientific literature, where important claims are often implicit, evolving, and internally debated. While large language models (LLMs) have shown impressive performance in information extraction and summarization, their ability to recover latent scientific consensus remains unclear. Here, we investigate this problem in the context of high-temperature superconductivity (HTS), a long-standing and highly debated topic in condensed matter physics, as a challenging testbed. Using near 18,000 highly-cited publications over the past seven decades, we construct a structured knowledge graph linking competing superconducting mechanisms, material families, evidential modalities, and citation relations. We find that LLM-extracted representations recover coherent and physically interpretable structures, including family-dependent mechanism profiles, evidence-specific correlations, and citation-mediated temporal evolution of scientific beliefs. Ablation studies on LLM further show that the global structure remains robust across prompting, decoding, and model variations. Our results suggest that LLMs can indeed serve as scalable tools for deciphering scientific knowledge in domains characterized by competing interpretations and evolving knowledge.
Digital Libraries (cs.DL); Machine Learning (cs.LG)
arXiv:2606.07570 [cs.DL]
(or arXiv:2606.07570v1 [cs.DL] for this version)
Full text under the publisher's open licence · 177 words