Computers’ Interpretations of Knowledge Representation Using Pre-Conceptual Schemas: An Approach Based on the BERT and Llama 2-Chat Models
Pre-conceptual schemas are a straightforward way to represent knowledge using controlled language regardless of context. Despite the benefits of using pre-conceptual schemas by humans, they present challenges when interpreted by computers. We propose an approach to making computers able to interpret...
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| Published in: | Big data and cognitive computing 2023-12, Vol.7 (4), p.182 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | eng |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Pre-conceptual schemas are a straightforward way to represent knowledge using controlled language regardless of context. Despite the benefits of using pre-conceptual schemas by humans, they present challenges when interpreted by computers. We propose an approach to making computers able to interpret the basic pre-conceptual schemas made by humans. To do that, the construction of a linguistic corpus is required to work with large language models—LLM. The linguistic corpus was mainly fed using Master’s and doctoral theses from the digital repository of the University of Nariño to produce a training dataset for re-training the BERT model; in addition, we complement this by explaining the elicited sentences in triads from the pre-conceptual schemas using one of the cutting-edge large language models in natural language processing: Llama 2-Chat by Meta AI. The diverse topics covered in these theses allowed us to expand the spectrum of linguistic use in the BERT model and empower the generative capabilities using the fine-tuned Llama 2-Chat model and the proposed solution. As a result, the first version of a computational solution was built to consume the language models based on BERT and Llama 2-Chat and thus automatically interpret pre-conceptual schemas by computers via natural language processing, adding, at the same time, generative capabilities. The validation of the computational solution was performed in two phases: the first one for detecting sentences and interacting with pre-conceptual schemas with students in the Formal Languages and Automata Theory course—the seventh semester of the systems engineering undergraduate program at the University of Nariño’s Tumaco campus. The second phase was for exploring the generative capabilities based on pre-conceptual schemas; this second phase was performed with students in the Object-oriented Design course—the second semester of the systems engineering undergraduate program at the University of Nariño’s Tumaco campus. This validation yielded favorable results in implementing natural language processing using the BERT and Llama 2-Chat models. In this way, some bases were laid for future developments related to this research topic. |
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| ISSN: | 2504-2289 2504-2289 |