AMGPT: A large language model for contextual querying in additive manufacturing
Generalized large language models (LLMs) such as GPT-4 may not provide specific answers to queries formulated by materials science researchers. These models may produce a high-level outline but lack the capacity to return detailed instructions on manufacturing and material properties of novel alloys...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Additive Manufacturing Letters |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772369024000409 |
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| _version_ | 1846127126703505408 |
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| author | Achuth Chandrasekhar Jonathan Chan Francis Ogoke Olabode Ajenifujah Amir Barati Farimani |
| author_facet | Achuth Chandrasekhar Jonathan Chan Francis Ogoke Olabode Ajenifujah Amir Barati Farimani |
| author_sort | Achuth Chandrasekhar |
| collection | DOAJ |
| description | Generalized large language models (LLMs) such as GPT-4 may not provide specific answers to queries formulated by materials science researchers. These models may produce a high-level outline but lack the capacity to return detailed instructions on manufacturing and material properties of novel alloys. We introduce “AMGPT”, a specialized LLM text generator designed for metal AM queries. The goal of AMGPT is to assist researchers and users in navigating a curated corpus of literature. Instead of training from scratch, we employ a pre-trained Llama2-7B model from Hugging Face in a Retrieval-Augmented Generation (RAG) setup, utilizing it to dynamically incorporate information from ∼50 AM papers and textbooks in PDF format. Mathpix is used to convert these PDF documents into TeX format, facilitating their integration into the RAG pipeline managed by LlamaIndex. A query retrieval function has also been added, enabling the system to fetch relevant literature from Elsevier journals based on the context of the query. Expert evaluations of this project highlight that specific embeddings from the RAG setup accelerate response times and maintain coherence in the generated text. |
| format | Article |
| id | doaj-art-e4fe90beec9e4d5c9efabedb9f46cd95 |
| institution | Kabale University |
| issn | 2772-3690 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Additive Manufacturing Letters |
| spelling | doaj-art-e4fe90beec9e4d5c9efabedb9f46cd952024-12-12T05:24:11ZengElsevierAdditive Manufacturing Letters2772-36902024-12-0111100232AMGPT: A large language model for contextual querying in additive manufacturingAchuth Chandrasekhar0Jonathan Chan1Francis Ogoke2Olabode Ajenifujah3Amir Barati Farimani4Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, 15213, PA, USAMechanical Engineering, Carnegie Mellon University, Pittsburgh, 15213, PA, USAMechanical Engineering, Carnegie Mellon University, Pittsburgh, 15213, PA, USAMechanical Engineering, Carnegie Mellon University, Pittsburgh, 15213, PA, USAMechanical Engineering, Carnegie Mellon University, Pittsburgh, 15213, PA, USA; Biomedical Engineering, Carnegie Mellon University, Pittsburgh, 15213, PA, USA; Chemical Engineering, Carnegie Mellon University, Pittsburgh, 15213, PA, USA; Machine Learning Department, Carnegie Mellon University, Pittsburgh, 15213, PA, USA; Correspondence to: 5000, Forbes Avenue, USA.Generalized large language models (LLMs) such as GPT-4 may not provide specific answers to queries formulated by materials science researchers. These models may produce a high-level outline but lack the capacity to return detailed instructions on manufacturing and material properties of novel alloys. We introduce “AMGPT”, a specialized LLM text generator designed for metal AM queries. The goal of AMGPT is to assist researchers and users in navigating a curated corpus of literature. Instead of training from scratch, we employ a pre-trained Llama2-7B model from Hugging Face in a Retrieval-Augmented Generation (RAG) setup, utilizing it to dynamically incorporate information from ∼50 AM papers and textbooks in PDF format. Mathpix is used to convert these PDF documents into TeX format, facilitating their integration into the RAG pipeline managed by LlamaIndex. A query retrieval function has also been added, enabling the system to fetch relevant literature from Elsevier journals based on the context of the query. Expert evaluations of this project highlight that specific embeddings from the RAG setup accelerate response times and maintain coherence in the generated text.http://www.sciencedirect.com/science/article/pii/S2772369024000409Large language modelsRetrieval-augmented generationMachine learningContextual queryingLaser powder bed fusion |
| spellingShingle | Achuth Chandrasekhar Jonathan Chan Francis Ogoke Olabode Ajenifujah Amir Barati Farimani AMGPT: A large language model for contextual querying in additive manufacturing Additive Manufacturing Letters Large language models Retrieval-augmented generation Machine learning Contextual querying Laser powder bed fusion |
| title | AMGPT: A large language model for contextual querying in additive manufacturing |
| title_full | AMGPT: A large language model for contextual querying in additive manufacturing |
| title_fullStr | AMGPT: A large language model for contextual querying in additive manufacturing |
| title_full_unstemmed | AMGPT: A large language model for contextual querying in additive manufacturing |
| title_short | AMGPT: A large language model for contextual querying in additive manufacturing |
| title_sort | amgpt a large language model for contextual querying in additive manufacturing |
| topic | Large language models Retrieval-augmented generation Machine learning Contextual querying Laser powder bed fusion |
| url | http://www.sciencedirect.com/science/article/pii/S2772369024000409 |
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