TrueGPT: can you privately extract algorithms from ChatGPT in tabular classification?
Recently, it has been shown that Large Language Models (LLMs) achieve impressive zero-shot classification on tabular data, revealing an internal algorithm ALLM without explicit training data. We predict that ALLM will become a standard for tabular data classification, replacing resource-intensive...
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sg-ntu-dr.10356-1756422024-05-06T15:36:58Z TrueGPT: can you privately extract algorithms from ChatGPT in tabular classification? Soegeng, Hans Farrell Thomas Peyrin School of Physical and Mathematical Sciences Adrien Benamira thomas.peyrin@ntu.edu.sg, adrien.benamira@ntu.edu.sg Computer and Information Science Mathematical Sciences Machine learning Explainable AI Large language models Recently, it has been shown that Large Language Models (LLMs) achieve impressive zero-shot classification on tabular data, revealing an internal algorithm ALLM without explicit training data. We predict that ALLM will become a standard for tabular data classification, replacing resource-intensive custom ML models. However, LLM complexity hinders regulatory transparency. To address this, we introduce a method to approximate ALLM with human-interpretable binary feature rules Aerule. We utilize the TT-rules (Truth Table rules) model developed by Benamira et al., 2023 to extract the binary rules through the LLM inference of tabular datasets. Following the extraction and approximation processes, we set aside the LLM and exclusively rely on Aerule for inference. Our method is fully automatic. We validate the approach on 8 public tabular datasets, adding a user option to activate privacy-preserving feature to ensure owner data protection. Bachelor's degree 2024-05-02T04:38:18Z 2024-05-02T04:38:18Z 2024 Final Year Project (FYP) Soegeng, H. F. (2024). TrueGPT: can you privately extract algorithms from ChatGPT in tabular classification?. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175642 https://hdl.handle.net/10356/175642 en application/pdf Nanyang Technological University |
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Computer and Information Science Mathematical Sciences Machine learning Explainable AI Large language models Soegeng, Hans Farrell TrueGPT: can you privately extract algorithms from ChatGPT in tabular classification? |
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Recently, it has been shown that Large Language Models (LLMs) achieve impressive
zero-shot classification on tabular data, revealing an internal algorithm ALLM without
explicit training data. We predict that ALLM will become a standard for tabular
data classification, replacing resource-intensive custom ML models. However, LLM
complexity hinders regulatory transparency. To address this, we introduce a method to
approximate ALLM with human-interpretable binary feature rules Aerule. We utilize the
TT-rules (Truth Table rules) model developed by Benamira et al., 2023 to extract the
binary rules through the LLM inference of tabular datasets. Following the extraction
and approximation processes, we set aside the LLM and exclusively rely on Aerule for
inference. Our method is fully automatic. We validate the approach on 8 public tabular
datasets, adding a user option to activate privacy-preserving feature to ensure owner
data protection. |
author2 |
Thomas Peyrin |
author_facet |
Thomas Peyrin Soegeng, Hans Farrell |
format |
Final Year Project |
author |
Soegeng, Hans Farrell |
author_sort |
Soegeng, Hans Farrell |
title |
TrueGPT: can you privately extract algorithms from ChatGPT in tabular classification? |
title_short |
TrueGPT: can you privately extract algorithms from ChatGPT in tabular classification? |
title_full |
TrueGPT: can you privately extract algorithms from ChatGPT in tabular classification? |
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TrueGPT: can you privately extract algorithms from ChatGPT in tabular classification? |
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TrueGPT: can you privately extract algorithms from ChatGPT in tabular classification? |
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truegpt: can you privately extract algorithms from chatgpt in tabular classification? |
publisher |
Nanyang Technological University |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/175642 |
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1800916303729393664 |