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Understanding Multi-Intent Spoken Language with Large Language Models


Core Concepts
Large Language Models (LLMs) can excel in multi-intent spoken language understanding by reconfiguring entity slots and introducing Sub-Intent Instructions (SII). The study explores the efficacy of LLMs in multi-intent SLU and introduces innovative metrics for evaluation.
Abstract
This study delves into harnessing Large Language Models (LLMs) for multi-intent spoken language understanding. By reconfiguring entity slots and introducing Sub-Intent Instructions (SII), the research showcases the potential of LLMs to outperform existing models. The study introduces novel metrics, Entity Slot Accuracy (ESA) and Combined Semantic Accuracy (CSA), to provide a comprehensive evaluation of LLM performance in multi-intent SLU. Through experiments on MixATIS and MixSNIPS datasets, the study highlights the competitive prowess of LLMs and their ability to enhance semantic frame parsing accuracy. The research addresses limitations such as quantization impact on performance and anticipates enhancements through data selection and prompt refinement. It proposes two new metrics, ESA and CSA, to evaluate LLMs in multi-intent SLU comprehensively. The study aims to catalyze further exploration into the potential of LLMs in advancing multi-intent SLU systems.
Stats
Our investigation reveals that Mistral-7B-Instruct-v0.1 model exhibits an ESA of 60.6%. In the MixSNIPS dataset, Llama-2-13B achieves an ESA of 83.6%.
Quotes
"To speak a language is to take on a world, a culture." - Frantz Fanon

Key Insights Distilled From

by Shangjian Yi... at arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04481.pdf
Do Large Language Model Understand Multi-Intent Spoken Language ?

Deeper Inquiries

Can Large Language Models effectively handle multi-intent SLU, and if so, how can they be adapted or constructed to fit within a multi-intent SLU framework?

Large Language Models (LLMs) have shown promising results in handling multi-intent Spoken Language Understanding (SLU). To adapt LLMs for multi-intent SLU, several key strategies can be employed. Firstly, entity slots need to be reconfigured to align with the generative nature of LLMs. This involves mapping traditional BIO-annotated slots into structured entity-slot mappings that are more conducive to the output generated by LLMs. Additionally, introducing Sub Intent Instructions (SII) can enhance the model's ability to dissect complex intents by segmenting utterances into individual sub-intents. Supervised fine-tuning is crucial for refining the model's performance on task-specific labeled datasets. Furthermore, incorporating techniques like QLoRA quantization can improve efficiency and scalability without compromising performance.

Does the size of a Large Language Model influence its performance in multi-intent SLU?

The size of a Large Language Model does impact its performance in multi-intent SLU tasks. Larger models tend to excel in environments with diverse domains due to their superior ability to capture and process intricate patterns present in data. However, it is essential to note that larger models may not always outperform smaller ones uniformly across all scenarios. In some cases, smaller models might exhibit competitive prowess depending on the complexity of detailed slot and intent recognition required in single-domain datasets.

How does sub-sentence segmentation influence Large Language Models' capacity for discerning complex intents?

Sub-sentence segmentation plays a crucial role in enhancing Large Language Models' capacity for discerning complex intents within utterances. By employing Sub Intent Instructions during training, models are directed towards parsing intricate intent and slot configurations present in multi-intent utterances more effectively. This methodology mirrors human cognitive processes by breaking down complex inquiries into manageable segments, enabling models to emulate corresponding thought processes accurately. This approach fosters nuanced perception and response formulation by focusing on detailed intent and slot configurations within sentences through sub-utterance directives during training sessions.
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