Summary of Balancing Accuracy and Efficiency in Multi-turn Intent Classification For Llm-powered Dialog Systems in Production, by Junhua Liu and Yong Keat Tan and Bin Fu and Kwan Hui Lim
Balancing Accuracy and Efficiency in Multi-Turn Intent Classification for LLM-Powered Dialog Systems in Production
by Junhua Liu, Yong Keat Tan, Bin Fu, Kwan Hui Lim
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents two novel approaches to enhance scalability and reduce latency in production dialogue systems for conversational AI. The first approach is Symbol Tuning, which simplifies intent labels to reduce task complexity and improve performance in multi-turn dialogues. The second approach is C-LARA (Consistency-aware, Linguistics Adaptive Retrieval Augmentation), a framework that employs Large Language Models (LLMs) for data augmentation and pseudo-labeling to generate synthetic multi-turn dialogues. These enriched datasets are used to fine-tune a small, efficient model suitable for deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making chatbots better at understanding what people want to talk about. It’s hard because there aren’t many good examples of conversations to learn from, and it’s hard to understand the context of what someone is saying. The researchers came up with two new ways to make this task easier. The first way is to simplify the labels we use to describe what people want to talk about. This makes it easier for computers to understand what people are saying. The second way is to generate more fake conversations that computers can learn from. This helps computers get better at understanding what people want to talk about. |
Keywords
» Artificial intelligence » Data augmentation
