Thread Detection and Response Generation using Transformers with Prompt Optimisation
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916155226062848 |
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| author | T, Kevin Joshua Agarwal, Arnav Sanjay, Shriya Sarda, Yash Alex, John Sahaya Rani Gupta, Saurav Kumar, Sushant Kamath, Vishwanath |
| author_facet | T, Kevin Joshua Agarwal, Arnav Sanjay, Shriya Sarda, Yash Alex, John Sahaya Rani Gupta, Saurav Kumar, Sushant Kamath, Vishwanath |
| contents | Conversational systems are crucial for human-computer interaction, managing complex dialogues by identifying threads and prioritising responses. This is especially vital in multi-party conversations, where precise identification of threads and strategic response prioritisation ensure efficient dialogue management. To address these challenges an end-to-end model that identifies threads and prioritises their response generation based on the importance was developed, involving a systematic decomposition of the problem into discrete components - thread detection, prioritisation, and performance optimisation which was meticulously analysed and optimised. These refined components seamlessly integrate into a unified framework, in conversational systems. Llama2 7b is used due to its high level of generalisation but the system can be updated with any open source Large Language Model(LLM). The computational capabilities of the Llama2 model was augmented by using fine tuning methods and strategic prompting techniques to optimise the model's performance, reducing computational time and increasing the accuracy of the model. The model achieves up to 10x speed improvement, while generating more coherent results compared to existing models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05931 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Thread Detection and Response Generation using Transformers with Prompt Optimisation T, Kevin Joshua Agarwal, Arnav Sanjay, Shriya Sarda, Yash Alex, John Sahaya Rani Gupta, Saurav Kumar, Sushant Kamath, Vishwanath Computation and Language Machine Learning I.2.7; I.2.6 Conversational systems are crucial for human-computer interaction, managing complex dialogues by identifying threads and prioritising responses. This is especially vital in multi-party conversations, where precise identification of threads and strategic response prioritisation ensure efficient dialogue management. To address these challenges an end-to-end model that identifies threads and prioritises their response generation based on the importance was developed, involving a systematic decomposition of the problem into discrete components - thread detection, prioritisation, and performance optimisation which was meticulously analysed and optimised. These refined components seamlessly integrate into a unified framework, in conversational systems. Llama2 7b is used due to its high level of generalisation but the system can be updated with any open source Large Language Model(LLM). The computational capabilities of the Llama2 model was augmented by using fine tuning methods and strategic prompting techniques to optimise the model's performance, reducing computational time and increasing the accuracy of the model. The model achieves up to 10x speed improvement, while generating more coherent results compared to existing models. |
| title | Thread Detection and Response Generation using Transformers with Prompt Optimisation |
| topic | Computation and Language Machine Learning I.2.7; I.2.6 |
| url | https://arxiv.org/abs/2403.05931 |