A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents
Ankan Mullick, Sombit Bose, Abhilash Nandy, Gajula Sai Chaitanya, Pawan Goyal
2024-11-01
Summary
This paper explores how large language models (LLMs) learn differently when trained for fast thinking versus slow thinking, focusing on the patterns of learning in their layers through gradient analysis.
What's the problem?
Understanding how LLMs learn is crucial for improving their performance, especially when they need to switch between quick responses and more thoughtful, detailed reasoning. However, there hasn't been much research into how these different types of thinking affect the learning process within the model's layers, particularly in terms of the gradients, which show how the model adjusts its learning.
What's the solution?
The authors analyze the gradients of different layers in LLMs when trained using fast thinking (quick responses) versus slow thinking (more detailed reasoning). They find that fast thinking results in larger and more erratic gradients, indicating instability, while slow thinking leads to more consistent gradients across layers, suggesting steadier learning. The study also shows that slow thinking helps the model better distinguish correct answers from incorrect ones compared to fast thinking.
Why it matters?
This research is important because it provides insights into how LLMs can be trained more effectively. By understanding the differences between fast and slow thinking in terms of gradient behavior, developers can create better training strategies that enhance the models' reasoning abilities and overall performance. This could lead to improvements in various applications where accurate and reliable responses are critical.
Abstract
In task-oriented dialogue systems, intent detection is crucial for interpreting user queries and providing appropriate responses. Existing research primarily addresses simple queries with a single intent, lacking effective systems for handling complex queries with multiple intents and extracting different intent spans. Additionally, there is a notable absence of multilingual, multi-intent datasets. This study addresses three critical tasks: extracting multiple intent spans from queries, detecting multiple intents, and developing a multi-lingual multi-label intent dataset. We introduce a novel multi-label multi-class intent detection dataset (MLMCID-dataset) curated from existing benchmark datasets. We also propose a pointer network-based architecture (MLMCID) to extract intent spans and detect multiple intents with coarse and fine-grained labels in the form of sextuplets. Comprehensive analysis demonstrates the superiority of our pointer network-based system over baseline approaches in terms of accuracy and F1-score across various datasets.