Evaluating Large Language Models for Optimized Intent Translation and Contradiction Detection Using KNN in IBN
Intent-Based Networking (IBN) simplifies network management by enabling users to express high-level intents in natural language, but existing approaches often fail to ensure alignment with network policies, leading to misconfigurations. Moreover, many methods lack robust validation mechanisms, reduc...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10855447/ |
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| author | Muhammad Asif Talha Ahmed Khan Wang-Cheol Song |
| author_facet | Muhammad Asif Talha Ahmed Khan Wang-Cheol Song |
| author_sort | Muhammad Asif |
| collection | DOAJ |
| description | Intent-Based Networking (IBN) simplifies network management by enabling users to express high-level intents in natural language, but existing approaches often fail to ensure alignment with network policies, leading to misconfigurations. Moreover, many methods lack robust validation mechanisms, reducing their reliability in dynamic environments. This research addresses these gaps by evaluating advanced Large Language Models (LLMs) such as BERT-base uncased (BERT-bu), GPT2, LLaMA3, Claude2 and small deep learning model BiLSTM with attention for translating intents and detecting contradictions. Using a curated dataset of 10,000 intent pairs, the proposed hybrid framework integrates a K-Nearest Neighbors (KNN) classifier to validate translations and recalibrate erroneous outputs. Experimental results demonstrate up to 5% higher accuracy (88%) and F1 scores compared to existing methods, ensuring precise intent translation and reliable network orchestration. This approach significantly enhances scalability and policy compliance in automated network environments. |
| format | Article |
| id | doaj-art-d671952782e74b2bbb8526e9a6e231fa |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d671952782e74b2bbb8526e9a6e231fa2025-01-31T23:04:47ZengIEEEIEEE Access2169-35362025-01-0113203162032710.1109/ACCESS.2025.353488010855447Evaluating Large Language Models for Optimized Intent Translation and Contradiction Detection Using KNN in IBNMuhammad Asif0https://orcid.org/0000-0002-1048-0912Talha Ahmed Khan1https://orcid.org/0000-0001-6411-163XWang-Cheol Song2https://orcid.org/0000-0002-7411-5316Department of Computer Engineering, Jeju National University, Jeju-si, Jeju-do, Republic of KoreaInstitute for Communication Systems, University of Surrey, Guildford, U.K.Department of Computer Engineering, Jeju National University, Jeju-si, Jeju-do, Republic of KoreaIntent-Based Networking (IBN) simplifies network management by enabling users to express high-level intents in natural language, but existing approaches often fail to ensure alignment with network policies, leading to misconfigurations. Moreover, many methods lack robust validation mechanisms, reducing their reliability in dynamic environments. This research addresses these gaps by evaluating advanced Large Language Models (LLMs) such as BERT-base uncased (BERT-bu), GPT2, LLaMA3, Claude2 and small deep learning model BiLSTM with attention for translating intents and detecting contradictions. Using a curated dataset of 10,000 intent pairs, the proposed hybrid framework integrates a K-Nearest Neighbors (KNN) classifier to validate translations and recalibrate erroneous outputs. Experimental results demonstrate up to 5% higher accuracy (88%) and F1 scores compared to existing methods, ensuring precise intent translation and reliable network orchestration. This approach significantly enhances scalability and policy compliance in automated network environments.https://ieeexplore.ieee.org/document/10855447/Intent-based networkingK-Nearest NeighborsNLPLLMGPTLLaMa |
| spellingShingle | Muhammad Asif Talha Ahmed Khan Wang-Cheol Song Evaluating Large Language Models for Optimized Intent Translation and Contradiction Detection Using KNN in IBN IEEE Access Intent-based networking K-Nearest Neighbors NLP LLM GPT LLaMa |
| title | Evaluating Large Language Models for Optimized Intent Translation and Contradiction Detection Using KNN in IBN |
| title_full | Evaluating Large Language Models for Optimized Intent Translation and Contradiction Detection Using KNN in IBN |
| title_fullStr | Evaluating Large Language Models for Optimized Intent Translation and Contradiction Detection Using KNN in IBN |
| title_full_unstemmed | Evaluating Large Language Models for Optimized Intent Translation and Contradiction Detection Using KNN in IBN |
| title_short | Evaluating Large Language Models for Optimized Intent Translation and Contradiction Detection Using KNN in IBN |
| title_sort | evaluating large language models for optimized intent translation and contradiction detection using knn in ibn |
| topic | Intent-based networking K-Nearest Neighbors NLP LLM GPT LLaMa |
| url | https://ieeexplore.ieee.org/document/10855447/ |
| work_keys_str_mv | AT muhammadasif evaluatinglargelanguagemodelsforoptimizedintenttranslationandcontradictiondetectionusingknninibn AT talhaahmedkhan evaluatinglargelanguagemodelsforoptimizedintenttranslationandcontradictiondetectionusingknninibn AT wangcheolsong evaluatinglargelanguagemodelsforoptimizedintenttranslationandcontradictiondetectionusingknninibn |