SOURCE-LINKED INTELLIGENCE
Retrieval-Augmented Multi-Prompt Ensemble for Minor-Grain Breeding Information Extraction
This paper presents our system for CCL2026-Eval Task 5: Minor-Grain Breeding Information Extraction (MGBIE), which jointly extracts 12 entity types and 6 relation types from minor-grain breeding literature. We propose RAME (Retrieval-Augmented Multi-Prompt Ensemble), a training-free framework that elicits multiple LLM outputs under controlled diversity and aggregates them by majority voting to obtain high-confidence predictions. RAME combines (i) retrieval-augmented few-shot selection via a hybrid BM25-embedding retriever, (ii) a three-prompt ensemble (Strict, Relaxed, Balanced) spanning the p
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-07T07:30:49.000Z
First collected: 2026-09-20T20:52:10.320Z. This is not the publication date.