arXiv:2601.00567v2 Announce Type: replace-cross Abstract: Adapting general-domain retrievers to scientific domains is challenging due to the scarcity of large-scale domain-specific relevance annotations and the substantial mismatch in vocabulary and information needs. Recent approaches address these issues through two independent directions that leverage large language models (LLMs): (1) generating synthetic queries for fine-tuning, and (2) generating auxiliary contexts to support relevance matching. However, both directions overlook the diverse academic concepts embedded within scientific doc
This development arises from ongoing research efforts to improve the efficiency and accuracy of information retrieval within specialized scientific domains, driven by the limitations of general-domain LLMs.
Improving scientific document retrieval directly enhances research efficiency, accelerates knowledge discovery, and enables more effective utilization of vast academic databases for innovation.
The proposed 'Academic Concept Index' introduces a novel approach to leverage embedded academic concepts, potentially bridging the gap between general-domain LLMs and specialized scientific information needs.
- · Academic researchers
- · Scientific publishers
- · AI/ML researchers in information retrieval
- · R&D intensive industries
- · Inefficient manual literature review processes
More precise and relevant search results for scientific inquiries will be delivered by AI-powered systems.
Accelerated pace of scientific discovery and technological innovation across various fields due to better access to existing knowledge.
Enhanced ability to identify untapped connections and interdisciplinary insights within the scientific literature, fostering new research directions.
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Read at arXiv cs.AI