mlsanityData to 7 Oct 2026

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

arXiv 2609.07134 ↗First posted 7 Sep 2026cs.CLcited by 0

On the map: Retrieval-augmented generation → Time series and agricultural RAG

Abstract (arXiv)

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 https://github.com/king-wang123/CCL26-RAME.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Time series and agricultural RAG, one of 15 directions in Retrieval-augmented generation. Builds on 12 of its 17 references that are in the map. First posted on arXiv 7 Sep 2026.

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12 of 17 references.

References · 12

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  4. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  5. 2023SGLang: Efficient Execution of Structured Language Model Programscited by 1,107
  6. 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 973
  7. 2022Unified Structure Generation for Universal Information Extractioncited by 220
  8. 2023InstructUIE: Multi-task Instruction Tuning for Unified Information Extractioncited by 167
  9. 2023GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extractioncited by 116
  10. 2025Parallel Scaling Law for Language Modelscited by 29
  11. 2025System Report for CCL25-Eval Task 10: SRAG-MAV for Fine-Grained Chinese Hate Speech Recognitioncited by 2
  12. 2026SuCo: Sufficiency-guided Continuous Adaptive Reasoningcited by 1

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