@inproceedings{yeniterzi-yeniterzi-2026-genaius-semeval,
    title = "{G}en{AI}us at {S}em{E}val-2026 Task 8: Beyond Retrieval with Relevance-Aware {RAG} for Faithful Multi-Turn Generation",
    author = "Yeniterzi, Suveyda  and
      Yeniterzi, Reyyan",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/bulk-corrections-2026-07-02/2026.semeval-1.328/",
    doi = "10.18653/v1/2026.semeval-1.328",
    pages = "2603--2610",
    ISBN = "979-8-89176-414-9",
    abstract = "This paper describes our submission to SemEval-2026 Task 8 on multi-turn retrieval-augmented generation (RAG). We propose a hybrid multi-stage pipeline that combines high-recall lexical retrieval, dual-embedding dense re-ranking with reciprocal rank fusion, LLM-based relevance judging, and strictly constrained evidence-grounded generation. Our design emphasizes robustness and faithfulness across the full retrieval-to-generation pipeline. Our results suggest that relevance-aware filtering and constrained generation are important for improving faithfulness and overall RAG performance."
}