# Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning

arXiv: https://arxiv.org/abs/2506.03035 · HTML: https://mlsanity.com/paper/2506.03035/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://mlsanity.com/paper/2112.01488/) (2021)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [SLURP: A Spoken Language Understanding Resource Package](https://mlsanity.com/paper/2011.13205/) (2020)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [Hermes 3 Technical Report](https://mlsanity.com/paper/2408.11857/) (2024)
- [Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model](https://mlsanity.com/paper/1907.02884/) (2019)
- [Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding](https://mlsanity.com/paper/2305.13512/) (2023)
- [Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU](https://mlsanity.com/paper/1910.00912/) (2019)
- [Towards Joint Intent Detection and Slot Filling via Higher-order Attention](https://mlsanity.com/paper/2109.08890/) (2021)
- [ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler](https://mlsanity.com/paper/2403.17536/) (2024)
- [CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding](https://mlsanity.com/paper/2406.10505/) (2024)
- [New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark](https://mlsanity.com/paper/2403.19727/) (2024)

## Abstract (arXiv)

Understanding user queries is fundamental in many applications, such as home assistants, booking systems, or recommendations. Accordingly, it is crucial to develop accurate Spoken Language Understanding (SLU) approaches to ensure the reliability of the considered system. Current State-of-the-Art SLU techniques rely on large amounts of training data; however, only limited annotated examples are available for specific tasks or languages. In the meantime, instruction-tuned large language models (LLMs) have shown exceptional performance on unseen tasks in a few-shot setting when provided with adequate prompts. In this work, we propose to explore example selection by leveraging Information retrieval (IR) approaches to build an enhanced prompt that is applied to an SLU task. We evaluate the effectiveness of the proposed method on several SLU benchmarks. Experimental results show that lexical IR methods significantly enhance performance without increasing prompt length.