Summary of Thread Detection and Response Generation Using Transformers with Prompt Optimisation, by Kevin Joshua T et al.
Thread Detection and Response Generation using Transformers with Prompt Optimisation
by Kevin Joshua T, Arnav Agarwal, Shriya Sanjay, Yash Sarda, John Sahaya Rani Alex, Saurav Gupta, Sushant Kumar, Vishwanath Kamath
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 A novel end-to-end model is developed for conversational systems, addressing the challenges of multi-party conversations by identifying threads and prioritizing response generation based on importance. This system involves a systematic decomposition into discrete components: thread detection, prioritization, and performance optimization. The refined components integrate seamlessly into a unified framework, leveraging Llama2 7b’s high generalization capabilities. However, the system can be updated with any open-source Large Language Model (LLM). Fine-tuning methods and strategic prompting techniques are used to optimize the model’s performance, reducing computational time and increasing accuracy. The model achieves up to 10x speed improvement while generating more coherent results compared to existing models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper develops a new way for computers to understand and respond to complex conversations between multiple people. It’s like a super-smart AI that can follow the flow of a conversation and decide what to say next based on how important it is. This helps conversations happen more efficiently and effectively. The researchers used a special kind of language model called Llama2 7b, which is very good at understanding many different types of text. They also came up with new ways to make the AI work better and faster, while still producing accurate responses. |
Keywords
* Artificial intelligence * Fine tuning * Generalization * Language model * Large language model * Optimization * Prompting
