@inproceedings{krishnasamy-wihardjo-2026-gigitai,
    title = "{G}igit{AI} at {S}em{E}val-2026 Task 8: Hybrid Sparse-Dense Retrieval and Zero-Shot Generation for Multi-Turn Conversational {RAG}",
    author = "Krishnasamy, Saran  and
      Wihardjo, Inez",
    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/ingest-acl-workshops/2026.semeval-1.389/",
    pages = "3103--3111",
    ISBN = "979-8-89176-414-9",
    abstract = "We describe our system for SemEval-2026 Task 8 (MTRAGEval) on multi-turn conversational RAG. Our approach combines hybrid retrieval (fusing SPLADE-v3 learned sparse representations with dense embeddings via Reciprocal Rank Fusion) with a fine-tuned cross-encoder reranker and zero-shot LLM generation using Claude Opus 4.5. We systematically evaluate 56 retrieval configurations across 4 domains, and 5 generation strategies across 5 LLMs. Our findings show that: (1) SPLADE-v3 with dataset rewrites substantially outperforms BM25 across all configurations, (2) simple zero-shot prompting matches sophisticated strategies like Self-RAG and CRAG, and (3) performance varies significantly by answerability class. On the test set, we achieve rank 5/29 on Task C (end-to-end RAG, H=0.5564), rank 7/26 on Task B (generation, H=0.7495), and rank 13/38 on Task A (retrieval, nDCG@5=0.4782). Our analysis reveals strong performance on answerable queries (H=0.685) but degradation on underspecified queries (H=0.254)."
}