ILLUMINER: Instruction-tuned Large Language Models for Few-shot Intent Classification and Slot Filling

Core Concepts

Instruct-LLMs outperform traditional models in IC and SF tasks with minimal training data.

Abstract

Introduction to IC and SF tasks in NLU.
Evaluation of Instruct-LLMs on benchmark datasets.
Comparison with baselines and ablation studies.
Discussion on challenges and advantages of Instruct-LLMs.

Stats

Large language models exhibit zero-shot performance.
FLAN-T5 11B model outperforms joint IC+SF methods.
Parameter-efficient fine-tuning requires less than 6% of training data.

Quotes

"Instruct-LLMs offer substantial benefits over traditional supervised approaches."

Key Insights Distilled From

ILLUMINER

by Paramita Mir... at arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17536.pdf

Deeper Inquiries

How can Instruct-LLMs be further optimized for multi-turn dialogue systems?

Instruct-LLMs can be optimized for multi-turn dialogue systems by incorporating context from previous turns to enhance the understanding of user intents and slots. One approach is to implement memory mechanisms that allow the model to retain information from past interactions and use it to inform the current dialogue. Additionally, fine-tuning Instruct-LLMs on datasets specifically designed for multi-turn conversations can help the model learn the nuances of maintaining context and coherence across multiple exchanges. Prompt engineering can also be tailored to include prompts that guide the model on how to handle multi-turn dialogues effectively.

What are the implications of hallucinations in LLMs for real-world applications?

Hallucinations in LLMs can have significant implications for real-world applications, especially in critical systems like task-oriented dialogue systems. When LLMs generate intents or slots that are not present in the candidate labels or user utterances, it can lead to incorrect responses and actions, potentially causing confusion or errors in the system's functionality. In scenarios where precise understanding and accurate predictions are crucial, such as in customer service or medical applications, hallucinations can result in misinformation or inappropriate responses. Addressing and mitigating hallucinations in LLMs is essential to ensure the reliability and effectiveness of these systems in practical use cases.

How can Instruct-LLMs be adapted for languages other than English?

Adapting Instruct-LLMs for languages other than English involves several key steps. Firstly, translating the task instructions and label descriptions into the target language is essential to ensure the model understands the input and output requirements accurately. Fine-tuning the Instruct-LLMs on multilingual datasets or cross-lingual instruction datasets can help the model learn to process and generate text in different languages effectively. Leveraging multilingual LLMs that have been pre-trained on diverse language data can also aid in adapting Instruct-LLMs for non-English languages. Additionally, incorporating language-specific prompts and examples during fine-tuning can enhance the model's performance and generalization capabilities across various languages.