TL;DR
This approach combines classical retrieval methods with the usage of multiple large language model (LLM) agents to generate concise, evidence-based reports to align with DRAGUN’s goal of supporting critical engagement with news.
Large language models answering questions over multi-page documents are expected to cite the supporting pages, yet supplied citations are sometimes inaccurate, and current evaluations score citations at generation time or against text passages: no existing benchmark evaluates whether a system can verify and correct a p...
Citation analysis has traditionally been organized around citation intent, function, stance, citation context analysis, and cited text span identification. Recent large language models (LLMs) have been applied to these tasks through prompting, in-context learning, fine-tuning, annotation assistance, and retrieval-augme...
Scientific discovery depends on finding prior literature that shapes what comes next. Existing retrieval systems optimize for relevance and popularity, often favoring central papers over less familiar works that later prove generative. We introduce \textbf{MUSES}, a million-instance benchmark for prospective intellectu...
This study presents a clear and reliable framework for classifying the intent behind scientific citations. It combines multi-model reasoning with concepts from social choice theory. Instead of using a single model, this framework employs three open Large Language Models Gemma, LLaMA, and Mistral. Additionally, we combi...
The main implication of this research is the validation of a practical architecture for improving IR systems, offering a viable alternative for domain-specific contexts such as Sequran.
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