arXiv:2605.24351v1 Announce Type: new Abstract: Large language models (LLMs) can support scientific literature synthesis, but remain prone to hallucinated references, uneven coverage, and weakly grounded thematic organization. We evaluate whether bibliometric structure improves LLM-assisted synthesis by comparing six pipelines for generating cluster descriptions under different levels of evidence and structure. Using 100 published bibliometric analyses, we reconstruct Scopus corpora, extract human-written cluster descriptions, and assess outputs by human alignment, semantic coverage, clusterin
The proliferation of LLMs creates an immediate need to evaluate their reliability and utility in complex, information-rich tasks like scientific literature synthesis.
This research provides critical insights into the limitations and potential methodologies for improving LLM performance in knowledge organization, directly impacting research efficiency and scientific discovery.
The understanding of how to structure LLM inputs to enhance output quality, moving beyond raw input toward more guided and evidence-based synthesis processes.
- · AI researchers focusing on retrieval-augmented generation and knowledge graph in
- · Academics and institutions employing LLMs for literature review and synthesis
- · Publishers and database providers seeking to improve bibliographic tools
- · Users relying on unguided or poorly structured LLM outputs for critical tasks
- · LLM developers who do not prioritize structured input and evidence grounding
Improved reliability and applicability of LLMs in academic and research settings for literature analysis.
Accelerated scientific discovery and knowledge synthesis due to more accurate and reliable automated tools.
Further integration of LLM-powered tools into scientific workflows, potentially redefining research methodologies and publication strategies.
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