Errors from the LLM and simulated algorithms.
<div><p>Large language models have revolutionized the field of natural language processing and are now becoming a one-stop solution to various tasks. In the field of Networking, LLMs can also play a major role when it comes to resource optimization and sharing. While Sumrate maximization...
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2025
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| _version_ | 1852017902141571072 |
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| author | Ali Abir Shuvro (21982871) |
| author2 | Md. Shahriar Islam Bhuiyan (21982874) Faisal Hussain (10731211) Md. Sakhawat Hossen (21982877) |
| author2_role | author author author |
| author_facet | Ali Abir Shuvro (21982871) Md. Shahriar Islam Bhuiyan (21982874) Faisal Hussain (10731211) Md. Sakhawat Hossen (21982877) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ali Abir Shuvro (21982871) Md. Shahriar Islam Bhuiyan (21982874) Faisal Hussain (10731211) Md. Sakhawat Hossen (21982877) |
| dc.date.none.fl_str_mv | 2025-08-04T17:31:00Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0329674.t004 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Errors_from_the_LLM_and_simulated_algorithms_/29823101 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Ecology Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified total d2d pairs total cellular users natural language processing maximum average efficiency empirical results suggest around 58 %, experiment also concludes using different combinations obtained using gpt shot performance analysis large language models shot analysis also play sumrate maximization stop solution resource optimization programming knowledge prior algorithmic major role generative power effective solution crucial factor art approaches |
| dc.title.none.fl_str_mv | Errors from the LLM and simulated algorithms. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Large language models have revolutionized the field of natural language processing and are now becoming a one-stop solution to various tasks. In the field of Networking, LLMs can also play a major role when it comes to resource optimization and sharing. While Sumrate maximization has been a crucial factor for resource optimization in the networking domain, the optimal or sub-optimal algorithms it requires can be cumbersome to comprehend and implement. An effective solution is leveraging the generative power of LLMs for such tasks where there is no necessity for prior algorithmic and programming knowledge. A zero-shot analysis of these models is necessary to define the feasibility of using them in such tasks. Using different combinations of total cellular users and total D2D pairs, our empirical results suggest that the maximum average efficiency of these models for sumrate maximization in comparison to state-of-the-art approaches is around 58%, which is obtained using GPT. The experiment also concludes that some variants of the large language models currently in use are not suitable for numerical and structural data without fine-tuning their parameters.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_e43ec04f24dfc2aa1eb615eb364f6a13 |
| identifier_str_mv | 10.1371/journal.pone.0329674.t004 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29823101 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Errors from the LLM and simulated algorithms.Ali Abir Shuvro (21982871)Md. Shahriar Islam Bhuiyan (21982874)Faisal Hussain (10731211)Md. Sakhawat Hossen (21982877)EcologyScience PolicyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedtotal d2d pairstotal cellular usersnatural language processingmaximum average efficiencyempirical results suggestaround 58 %,experiment also concludesusing different combinationsobtained using gptshot performance analysislarge language modelsshot analysisalso playsumrate maximizationstop solutionresource optimizationprogramming knowledgeprior algorithmicmajor rolegenerative powereffective solutioncrucial factorart approaches<div><p>Large language models have revolutionized the field of natural language processing and are now becoming a one-stop solution to various tasks. In the field of Networking, LLMs can also play a major role when it comes to resource optimization and sharing. While Sumrate maximization has been a crucial factor for resource optimization in the networking domain, the optimal or sub-optimal algorithms it requires can be cumbersome to comprehend and implement. An effective solution is leveraging the generative power of LLMs for such tasks where there is no necessity for prior algorithmic and programming knowledge. A zero-shot analysis of these models is necessary to define the feasibility of using them in such tasks. Using different combinations of total cellular users and total D2D pairs, our empirical results suggest that the maximum average efficiency of these models for sumrate maximization in comparison to state-of-the-art approaches is around 58%, which is obtained using GPT. The experiment also concludes that some variants of the large language models currently in use are not suitable for numerical and structural data without fine-tuning their parameters.</p></div>2025-08-04T17:31:00ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0329674.t004https://figshare.com/articles/dataset/Errors_from_the_LLM_and_simulated_algorithms_/29823101CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/298231012025-08-04T17:31:00Z |
| spellingShingle | Errors from the LLM and simulated algorithms. Ali Abir Shuvro (21982871) Ecology Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified total d2d pairs total cellular users natural language processing maximum average efficiency empirical results suggest around 58 %, experiment also concludes using different combinations obtained using gpt shot performance analysis large language models shot analysis also play sumrate maximization stop solution resource optimization programming knowledge prior algorithmic major role generative power effective solution crucial factor art approaches |
| status_str | publishedVersion |
| title | Errors from the LLM and simulated algorithms. |
| title_full | Errors from the LLM and simulated algorithms. |
| title_fullStr | Errors from the LLM and simulated algorithms. |
| title_full_unstemmed | Errors from the LLM and simulated algorithms. |
| title_short | Errors from the LLM and simulated algorithms. |
| title_sort | Errors from the LLM and simulated algorithms. |
| topic | Ecology Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified total d2d pairs total cellular users natural language processing maximum average efficiency empirical results suggest around 58 %, experiment also concludes using different combinations obtained using gpt shot performance analysis large language models shot analysis also play sumrate maximization stop solution resource optimization programming knowledge prior algorithmic major role generative power effective solution crucial factor art approaches |