Conversational Factor Information Retrieval Model (ConFIRM)
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866912063371083776 |
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| author | Choi, Stephen Gazeley, William Wong, Siu Ho Li, Tingting |
| author_facet | Choi, Stephen Gazeley, William Wong, Siu Ho Li, Tingting |
| contents | This paper introduces the Conversational Factor Information Retrieval Method (ConFIRM), a novel approach to fine-tuning large language models (LLMs) for domain-specific retrieval tasks. ConFIRM leverages the Five-Factor Model of personality to generate synthetic datasets that accurately reflect target population characteristics, addressing data scarcity in specialized domains. We demonstrate ConFIRM's effectiveness through a case study in the finance sector, fine-tuning a Llama-2-7b model using personality-aligned data from the PolyU-Asklora Fintech Adoption Index. The resulting model achieved 91% accuracy in classifying financial queries, with an average inference time of 0.61 seconds on an NVIDIA A100 GPU. ConFIRM shows promise for creating more accurate and personalized AI-driven information retrieval systems across various domains, potentially mitigating issues of hallucinations and outdated information in LLMs deployed |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_13001 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Conversational Factor Information Retrieval Model (ConFIRM) Choi, Stephen Gazeley, William Wong, Siu Ho Li, Tingting Information Retrieval Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language Machine Learning This paper introduces the Conversational Factor Information Retrieval Method (ConFIRM), a novel approach to fine-tuning large language models (LLMs) for domain-specific retrieval tasks. ConFIRM leverages the Five-Factor Model of personality to generate synthetic datasets that accurately reflect target population characteristics, addressing data scarcity in specialized domains. We demonstrate ConFIRM's effectiveness through a case study in the finance sector, fine-tuning a Llama-2-7b model using personality-aligned data from the PolyU-Asklora Fintech Adoption Index. The resulting model achieved 91% accuracy in classifying financial queries, with an average inference time of 0.61 seconds on an NVIDIA A100 GPU. ConFIRM shows promise for creating more accurate and personalized AI-driven information retrieval systems across various domains, potentially mitigating issues of hallucinations and outdated information in LLMs deployed |
| title | Conversational Factor Information Retrieval Model (ConFIRM) |
| topic | Information Retrieval Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2310.13001 |