We introduce SciRet, a compute-aware empirical study of retrieval-augmented generation for scientific question answering over CORD-19. Rather than proposing a new model, we evaluate a fixed scientific RAG pipeline across three corpus scales: 1,034 chunks (1K papers), 5,160 chunks (5K papers), and 15,480 chunks (15K papers). The pipeline combines sentence-window chunking, BM25, BGE-M3 dense retrieval, reciprocal rank fusion, optional cross-encoder reranking, and grounded answer generation. Across these settings, hybrid retrieval is more robust than either sparse-only or dense-only retrieval in our setting, reaching Recall@10 of 1.000 at 1K and 15K. In contrast, an MS MARCO-trained cross-encoder reranker reduces precision on the scientific corpus, suggesting that domain mismatch can outweigh the benefits of stronger query-passage interaction. Generation faithfulness measured with RAGAS increases with corpus scale in our setup. Retrieval evaluation uses pseudo-relevance labels derived from the hybrid system, so we treat the results as controlled comparative evidence rather than a benchmark claim. We release code, indexes, and evaluation outputs to support replication and follow-up studies.
The Adaptive Multi-Stage Vector Retrieval (AMSVR) framework is proposed, prioritising weighted, drift-resistant composition over uniform fusion, and offers tailored configurations: AMSVR-Scientific (dense + tuned hybrid) peaks at NDCG@10 = 0.7570 on SciFact, while AMSVR-Full (seven stages) targets broader, noisier corpora where Recall@100 matters most.
Samsudeen Alabi Bankole, Yakub Kayode Saheed· NLP & Big Data· 0 citations
The core finding is that RAG hurts rather than helps: the LLM-only baseline achieves 78.6% accuracy, outperforming the full system at 78.5% (McNemar’s test, p = 0 . 962).
This paper presents the IIMAS-RAG system, which evaluates multi-turn retrieval-augmented generation (RAG) conversations, a modular pipeline composed of LLM-based query rewriting to transform conversational history into standalone queries, and hybrid sparse–dense retrieval combining SPLADE and Voyage-3-large via Reciprocal Rank Fusion (RRF).
Vania Raya-Rios, Helena Gómez-Adorno, Leon Hecht et al.· SemEval@ACL· 1 citation
This work presents Q U DAR, a dual-perspective adaptive retrieval framework motivated by a systematic analysis of retrieval behavior across retriever type and query format, enabling effective integration of complementary retrieval signals while mitigating noise.
Joeun Kim, Seunghyouk Yoon, Xuan-Bach Le et al.· Annual Meeting of the Associ...· 1 citation
This work proposes Trident, with two complementary components: Trident-R, a retriever-agnostic LLM reranker that converts each candidate into an LLM-readable semantic record, then performs a single adaptive-K rerank call; and Trident-S, a generation-side module that prompts the VLM under topical, entity, and structural lenses before synthesis.
Guanchen Wu, Jiayuan Ding, Subhabrata Mukherjee et al.· 0 citations
