Summary of An Approach to Build Zero-shot Slot-filling System For Industry-grade Conversational Assistants, by G P Shrivatsa Bhargav et al.
An Approach to Build Zero-Shot Slot-Filling System for Industry-Grade Conversational Assistants
by G P Shrivatsa Bhargav, Sumit Neelam, Udit Sharma, Shajith Ikbal, Dheeraj Sreedhar, Hima Karanam, Sachindra Joshi, Pankaj Dhoolia, Dinesh Garg, Kyle Croutwater, Haode Qi, Eric Wayne, J William Murdock
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper presents an approach to build a Large Language Model (LLM) based slot-filling system for Dialogue State Tracking in conversational assistants. The system must use smaller-sized models to meet low latency requirements and enable cloud and customer premise deployments, as well as possess zero-shot capabilities across various domains and scenarios. A fine-tuning approach is adopted, where a pre-trained LLM is fine-tuned into a slot-filling model using task-specific data. The paper provides details on the data preparation and model building process, as well as an analysis of experimental evaluations. The results show a 6.9% relative improvement in F1 metric over the best baseline on a realistic benchmark, with a reduced latency by 57%. Additionally, the prepared data improves F1 by 4.2% relative across various slot-types. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about building a special kind of computer model that helps chatbots understand what people are saying. The goal is to make this model work fast and be able to understand conversations in different areas like shopping or healthcare. To do this, the researchers used a pre-trained model and adjusted it to fit the specific tasks they wanted it to handle. They also made sure their data was good enough to help the model learn well. After testing the model, they found that it did much better than previous models and could understand conversations quickly. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Tracking » Zero shot
