2026· SemEval@ACL· pp. 3137-3142· 1 citation· 11 references
Computer Science
TL;DR
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).
Abstract
We present a trust-weighted Retrieval-Augmented Generation (RAG; Lewis et al., 2020) system for SemEval-2026 Task 7 (BLEnD) Track 2 (Ousidhoum et al., 2026), targeting English cultural multiple-choice QA across 30 countries. Built atop Llama-3.1-8B-Instruct (Meta AI, 2024), the six-phase pipeline integrates hybrid BM25+FAISS retrieval, country-aware filtering, intent detection, tiered routing, anti-leak prompt engineering, and trust-weighted reranking. 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 ). Oracle analysis reveals that only 40.7% of questions are answerable from the knowledge base, explaining why retrieval introduces more noise than signal. The sole recovery comes from anti-leak prompt filtering (Phase 4), which mitigates answer-anchoring artifacts. Code: https://github.com/CultRAG/ BLEnD-CultRAG .
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 pap...
Kaysarul Anas Apurba, Mahade Hasan, Rofiqul Alam Shehab et al.· 0 citations
Error analysis shows that a non-trivial fraction of failures are placeholder strings caused by API errors rather than incorrect generations, and that surface-level mismatches (verbosity, ortho-graphic variation) account for many of the remaining errors.
These findings establish learned sparse retrieval as a highly impactful design choice in automated fact-checking, with retrieval quality serving as a critical determinant of end-to-end performance in the studied systems.
Ritvik Setty, Vinay Setty· International Conference on...· 0 citations
Overall, legal information processing benefits from different inductive biases across tasks, with cross-architecture ensembling, feature-based reranking and retrieval-augmented prompting each proving most effective in different settings.
Amal Saad Alshehri, N. Bencomo, Amir Atapour-Abarghouei· 0 citations
This paper presents a technical synthesis of RAG for trustworthy enterprise assistants and describes the end-to-end pipeline in full: document chunking, embedding, vector indexing, retrieval, cross-encoder re-ranking, and grounded generation with inline citations.
Bini P. B.· International Journal of Inf...· 0 citations
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 corp...
Samsudeen Alabi Bankole, Yakub Kayode Saheed· NLP & Big Data· 0 citations
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