Retrieval-Augmented Multi-Prompt Ensemble for Minor-Grain Breeding Information Extraction

Abstract: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 precision to recall spectrum, and (iii) large-scale repeated sampling with majority voting to filter noisy predictions. Built on DeepSeek-V4-Flash, RAME achieves a Total Score of 0.499 (NER 0.730, RE 0.346) on the leaderboard, ranking 1st and surpassing the official Track-A baseline powered by GPT-5.5 (0.448), representing an 11.4% relative improvement. Code is available at this https URL.

Computation and Language (cs.CL)

arXiv:2609.07134 [cs.CL]

(or arXiv:2609.07134v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.07134 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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