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Qwen 2.5 powers multi-turn retrieval system to top SemEval ranks

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed a three-stage retrieval system for multi-turn conversations, enhancing accuracy in information retrieval tasks. The system first refines context-dependent queries using a fine-tuned Qwen 2.5 7B model to create standalone questions. It then employs a hybrid search combining BM25 and dense vector retrieval, fused with Reciprocal Rank Fusion, before a cross-encoder model reranks the results for improved precision. This approach achieved a notable nDCG@5 score in a recent SemEval task, outperforming many other systems. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Improves multi-turn conversational search accuracy by combining advanced query rewriting, hybrid search, and cross-encoder reranking.

RANK_REASON Academic paper detailing a novel system for a benchmark task.

Read on arXiv cs.CL →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 · Gheorghe Cosmin Silaghi ·

    Caraman at SemEval-2026 Task 8: Three-Stage Multi-Turn Retrieval with Query Rewriting, Hybrid Search, and Cross-Encoder Reranking

    We describe our system for SemEval-2026 Task 8 (MTRAGEval), participating in Task A (Retrieval) across four English-language domains. Our approach employs a three-stage pipeline: (1) query rewriting via a LoRA-fine-tuned Qwen 2.5 7B model that transforms context-dependent follow-…

  2. dev.to — LLM tag TIER_1 · 丁久 ·

    RAG Retrieval Optimization: Hybrid Search, Re-Ranking, Query Transformation

    <blockquote> <p><em>This article was originally published on <a href="https://dingjiu1989-hue.github.io/en/ai/rag-retrieval-optimization.html" rel="noopener noreferrer">AI Study Room</a>. For the full version with working code examples and related articles, visit the original pos…

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