# Retrieval-Enhanced Few-Shot Prompting for Speech Event Extraction

arXiv: https://arxiv.org/abs/2504.21372 · HTML: https://mlsanity.com/paper/2504.21372/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Structured Prediction as Translation between Augmented Natural Languages](https://mlsanity.com/paper/2101.05779/) (2021)
- [Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2108.13161/) (2021)
- [Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://mlsanity.com/paper/1809.09078/) (2018)
- [Cross-media Structured Common Space for Multimedia Event Extraction](https://mlsanity.com/paper/2005.02472/) (2020)
- [Joint Extraction of Events and Entities within a Document Context](https://mlsanity.com/paper/1609.03632/) (2016)
- [LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model](https://mlsanity.com/paper/2304.06248/) (2023)
- [Joint Multimedia Event Extraction from Video and Article](https://mlsanity.com/paper/2109.12776/) (2021)
- [OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction](https://mlsanity.com/paper/2209.02693/) (2022)
- [SpeechEE: A Novel Benchmark for Speech Event Extraction](https://mlsanity.com/paper/2408.09462/) (2024)

## Built on by (most cited first, 1 of 1)

- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)

## Abstract (arXiv)

Speech Event Extraction (SpeechEE) is a challenging task that lies at the intersection of Automatic Speech Recognition (ASR) and Natural Language Processing (NLP), requiring the identification of structured event information from spoken language. In this work, we present a modular, pipeline-based SpeechEE framework that integrates high-performance ASR with semantic search-enhanced prompting of Large Language Models (LLMs). Our system first classifies speech segments likely to contain events using a hybrid filtering mechanism including rule-based, BERT-based, and LLM-based models. It then employs few-shot LLM prompting, dynamically enriched via semantic similarity retrieval, to identify event triggers and extract corresponding arguments. We evaluate the pipeline using multiple LLMs (Llama3-8B, GPT-4o-mini, and o1-mini) highlighting significant performance gains with o1-mini, which achieves 63.3% F1 on trigger classification and 27.8% F1 on argument classification, outperforming prior benchmarks. Our results demonstrate that pipeline approaches, when empowered by retrieval-augmented LLMs, can rival or exceed end-to-end systems while maintaining interpretability and modularity. This work provides practical insights into LLM-driven event extraction and opens pathways for future hybrid models combining textual and acoustic features.