{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T22:00:14Z","timestamp":1771538414240,"version":"3.50.1"},"reference-count":36,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3661118","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T20:51:07Z","timestamp":1770411067000},"page":"23227-23243","source":"Crossref","is-referenced-by-count":0,"title":["Personalized Response Generation in Large Language Models via Lightweight Preference Optimization and Dynamic Context Integration"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9122-7676","authenticated-orcid":false,"given":"Zahra","family":"Jamali","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7238-8209","authenticated-orcid":false,"given":"Morteza","family":"Keshtkaran","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7113-5197","authenticated-orcid":false,"given":"Mohsen","family":"Raji","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.2196\/mental.7785"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3102\/0034654315581420"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3025453.3025496"},{"key":"ref4","article-title":"GPT-4 technical report","volume-title":"arXiv:2303.08774","author":"Achiam","year":"2023"},{"key":"ref5","article-title":"LLaMA: Open and efficient foundation language models","author":"Touvron","year":"2023","journal-title":"arXiv:2302.13971"},{"key":"ref6","article-title":"Mistral 7B","author":"Jiang","year":"2023","journal-title":"arXiv:2310.06825"},{"key":"ref7","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Brown"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1205"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1094"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1542"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3560815"},{"key":"ref12","article-title":"A systematic survey of prompt engineering in large language models: Techniques and applications","author":"Sahoo","year":"2024","journal-title":"arXiv:2402.07927"},{"key":"ref13","first-page":"9459","article-title":"Retrieval-augmented generation for knowledge-intensive NLP tasks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Lewis"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3115\/1073336.1073339"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.14"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.24"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1609\/aaaiss.v3i1.31203"},{"key":"ref19","article-title":"Wizard of wikipedia: knowledge-powered conversational agents","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dinan"},{"key":"ref20","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ouyang"},{"key":"ref21","first-page":"3008","article-title":"Learning to summarize from human feedback","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Stiennon"},{"key":"ref22","article-title":"Proximal policy optimization algorithms","author":"Schulman","year":"2017","journal-title":"arXiv:1707.06347"},{"key":"ref23","article-title":"Direct preference optimization: Your language model is secretly a reward model","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Rafailov"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.626"},{"key":"ref25","volume-title":"TRL: Transformer Reinforcement Learning","author":"von Werra","year":"2020"},{"key":"ref26","article-title":"Poly-encoders: Architectures and pre-training strategies for fast and accurate multi-sentence scoring","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Humeau"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acldemos.30"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2019.2921572"},{"key":"ref29","article-title":"LoRA: Low-rank adaptation of large language models","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Hu"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i10.21326"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-29135-8_7"},{"key":"ref32","article-title":"The llama 3 herd of models","author":"Grattafiori","year":"2024","journal-title":"arXiv:2407.21783"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3115\/1073083.1073135"},{"key":"ref34","first-page":"74","article-title":"ROUGE: A package for automatic evaluation of summaries","volume-title":"Proc. Text Summarization Branches Out","author":"Lin"},{"key":"ref35","article-title":"BERTScore: Evaluating text generation with BERT","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zhang"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.131"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11372692.pdf?arnumber=11372692","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T21:00:01Z","timestamp":1771534801000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11372692\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":36,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3661118","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}