PapersRAG 1.5B 🧪 A retrieval augmented generation system for querying recent scientific literature — continuously updated. PapersRAG 1.5B helps researchers explore and answer questions across a growing corpus of recent NLP papers from arXiv. It pairs a lightweight language model with a curated knowledge base of paper abstracts and a retrieval pipeline that prioritizes faithful, citation backed answers over hallucination. The model is automatically refreshed every day with the latest cs.CL papers. The knowledge base expands on its own. No manual upkeep required. Model description Type: Retrieval augmented generation (RAG) Base language model: Qwen 2.5 1.5B — small, fast, coherent when grounded with good context Knowledge base: A continuously growing collection of abstracts from the most recent cs.CL papers on arXiv, updated daily via an automated pipeline Retrieval pipeline: Dense embeddings for initial candidate retrieval, cross encoder for re ranking — only the most relevant chunks reach the language model Answer style: Every answer cites the paper title it draws from. If no relevant paper is found, the model says so instead of fabricating one Intended use PapersRAG is a research…
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