Personalized Text Generation with LLMs: A Systematic Literature Review with a Focus on Healthcare
Abstract
Large Language Models (LLMs) produce generic responses that fail to account for individual preferences and contexts. Personalized LLMs address this by leveraging user-specific data to generate tailored outputs. This paper presents a systematic literature review of 52 studies on personalized text generation, published between 2022 and 2025, with focus on healthcare applications.This study follows Kitchenham’s guidelines for planning and conducting systematic literature reviews (SLRs), while the reporting of the review process adheres to the PRISMA framework. The analysis focuses on techniques, models, metrics, and challenges identified in the literature. The results indicate that prompting is the most adopted technique (55.8%), followed by finetuning (46.2%) and RAG (26.9%). Health applications remain underrepresented (9.6%) but are rapidly emerging, covering tasks such as personalized medical responses and mental health assistance. Key challenges include the lack of personalization-aware metrics, static preference assumptions, and privacy concerns.
References
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Balepur, N., Padmakumar, V., Yang, F., Feng, S., Rudinger, R., and Boyd-Graber, J. L. (2025). Whose boat does it float? improving personalization in preference tuning via inferred user personas. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3371–3393.
Dai, Z., Yao, C., Han, W., Yuanying, Y., Gao, Z., and Chen, J. (2024). Mpcoder: Multi-user personalized code generator with explicit and implicit style representation learning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3765–3780.
Fendji, J. L. K., Donatien, D., and Atemkeng, M. (2025). Hybrid profile based multi-document text summarisation. Procedia Computer Science, 252:862–872.
Gui, H. and Wang, Z. (2024). Response generation with personal attributes and act information. In 2024 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE.
Gupta, A., Hussain, M., Nikhileshwar, K., Rastogi, A., and Rangarajan, K. (2025). Integrating large language models into radiology workflow: Impact of generating personalized report templates from summary. European Journal of Radiology, 189:112198.
Han, A., Koushik, N., Bidarkundi, N., Shehata, M. N., Kunchipudi, V., Mammadli, T., Mehta, S., and Lerner, O. (2024). Enhancing llm conversational acuity using pragmatic measures. In 2024 15th International Conference on Information and Communication Technology Convergence (ICTC), pages 886–891. IEEE.
Hang, C. N., Tan, C. W., and Yu, P.-D. (2024). Mcqgen: A large language model-driven mcq generator for personalized learning. IEEE Access, 12:102261–102273.
Hu, Z., Zhao, H., Zhao, Y., Xu, S., and Xu, B. (2024). T-agent: A term-aware agent for medical dialogue generation. In 2024 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE.
Huang, B.-S., Kuo, T. T., Wang, L.-J., and Lin, C.-Y. (2025). Remind: Recall enhanced memory integration for natural language dialogue systems. In 2025 IEEE Conference on Artificial Intelligence (CAI), pages 1–6. IEEE.
Huang, Q., Liu, X., Ko, T., Wu, B., Wang, W., Zhang, Y., and Tang, L. (2024). Selective prompting tuning for personalized conversations with llms. In Findings of the Association for Computational Linguistics: ACL 2024, pages 16212–16226.
Huq, F., Samee, A., Lin, D. C.-E., Tang, A. X., and Bigham, J. P. (2025). Noteeline: Supporting real-time, personalized notetaking with llm-enhanced micronotes. In Proceedings of the 30th International Conference on Intelligent User Interfaces, pages 1064–1081.
Ji, B. (2023). Based on text augmentation personalized dialog generation with persona-sparse data. In 2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), pages 717–720. IEEE.
Ji, K., Lian, Y., Li, L., Gao, J., Li, W., and Dai, B. (2025). Enhancing persona consistency for llms’ role-playing using persona-aware contrastive learning. In Findings of the Association for Computational Linguistics: ACL 2025, pages 26221–26238.
Joko, H., Chatterjee, S., Ramsay, A., De Vries, A. P., Dalton, J., and Hasibi, F. (2024). Doing personal laps: Llm-augmented dialogue construction for personalized multi-session conversational search. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 796–806.
Ju, S. and Wang, C. (2024). Beyond simple text style transfer: Unveiling compound text style transfer with prompt-based pre-trained language models. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6850–6854. IEEE.
Ke, S.-W. and Chen, W.-L. (2021). Stylized dialogue generation. In 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pages 1456–1460. IEEE.
Kim, T., Agarwal, D., Ackerman, J., and Saha, M. (2025). Steering ai-driven personalization of scientific text for general audiences. Proceedings of the ACM on Human-Computer Interaction, 9(7):1–28.
Kirstein, F., Ruas, T., Kratel, R., and Gipp, B. (2024). Tell me what i need to know: Exploring llm-based (personalized) abstractive multi-source meeting summarization. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 920–939.
Kitchenham, B. et al. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1–26.
Lee, G., Jeong, M., Kim, Y., Jung, H., Oh, J., Kim, S., and Yun, S.-Y. (2024a). Bapo: Base-anchored preference optimization for overcoming forgetting in large language models personalization. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 6804–6820.
Lee, G. H., Lee, K. J., Jeong, B., and Kim, T. (2024b). Developing personalized marketing service using generative ai. Ieee Access, 12:22394–22402.
Li, C., Zhang, M., Mei, Q., Kong, W., and Bendersky, M. (2024). Learning to rewrite prompts for personalized text generation. In Proceedings of the ACM Web Conference 2024, pages 3367–3378.
Lian, J., Ao, X., Liu, X., Liu, Y., and He, Q. (2025). Panoramic interests: Stylistic-content aware personalized headline generation. In Companion Proceedings of the ACM on Web Conference 2025, pages 1109–1112.
Liu, D., Wu, Z., Song, D., and Huang, H.-Y. (2025a). A persona-aware llm-enhanced framework for multi-session personalized dialogue generation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 103–123.
Liu, J., Qiu, Z., Li, Z., Dai, Q., Yu, W., Zhu, J., Hu, M., Yang, M., Chua, T.-S., and King, I. (2025b). A survey of personalized large language models: Progress and future directions. arXiv preprint arXiv:2502.11528.
Liu, J., Zhu, Y., Wang, S., Wei, X., Min, E., Lu, Y., Wang, S., Yin, D., and Dou, Z. (2025c). Llms+ persona-plug= personalized llms. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9373–9385.
Neupane, S., Mitra, S., Mittal, S., Gaur, M., Golilarz, N. A., Rahimi, S., and Amirlatifi, A. (2025). Medinsight: A multi-source context augmentation framework for generating patient-centric medical responses using large language models. ACM Transactions on Computing for Healthcare, 6(2):1–19.
Nezhad, M. M. and Kangavari, M. (2024). Personalized persuasive text generation. In 2024 10th International Conference on Artificial Intelligence and Robotics (QICAR), pages 261–267. IEEE.
Nezhad, M. M., Kisomi, M. A., and Gholinezhad, F. (2025). Adaptive persuasion in conversational ai: An llm-driven framework for dynamic strategy switching via personality and sentiment analysis. In 2025 11th International Conference on Web Research (ICWR), pages 145–149. IEEE.
Oh, M., Lee, S., and Ok, J. (2025). Comparison-based active preference learning for multi-dimensional personalization. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 33145–33166.
Padmakumar, V., Wang, Z., Arbour, D., and Healey, J. (2025). Principled content selection to generate diverse and personalized multi-document summaries. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 29884–29899.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021). The prisma 2020 statement: an updated guideline for reporting systematic reviews. bmj, 372.
Prahlad, D., Lee, C., Kim, D., and Kim, H. (2025). Personalizing large language models using retrieval augmented generation and knowledge graph. In Companion Proceedings of the ACM on Web Conference 2025, pages 1259–1263.
Qiu, Y., Zhao, X., Zhang, Y., Bai, Y., Wang, W., Cheng, H., Feng, F., and Chua, T.-S. (2025). Measuring what makes you unique: Difference-aware user modeling for enhancing llm personalization. In Findings of the Association for Computational Linguistics: ACL 2025, pages 21258–21277.
Ryan, M. J., Shaikh, O., Bhagirath, A., Frees, D., Held, W. B., and Yang, D. (2025). Synthesizeme! inducing persona-guided prompts for personalized reward models in llms. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8045–8078.
Salemi, A., Kallumadi, S., and Zamani, H. (2024a). Optimization methods for personalizing large language models through retrieval augmentation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 752–762.
Salemi, A., Mysore, S., Bendersky, M., and Zamani, H. (2024b). Lamp: When large language models meet personalization. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7370–7392.
Shafi, F. R., Hossain, M. A., and Choudhury, S. (2025). Personalized mental health assistance with large language models. In 2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC), pages 815–823. IEEE.
Srivastava, H., Sunil, S., and Kumari, K. S. (2022). Neural text style transfer with custom language styles for personalized communication systems. In 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), pages 1–6. IEEE.
Tan, Z., Liu, Z., and Jiang, M. (2024a). Personalized pieces: Efficient personalized large language models through collaborative efforts. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 6459–6475.
Tan, Z., Zeng, Q., Tian, Y., Liu, Z., Yin, B., and Jiang, M. (2024b). Democratizing large language models via personalized parameter-efficient fine-tuning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 6476–6491.
Tang, Y., Wang, B., Zhao, D., Jinxiaojia, J., Zhangjijun, Z., He, R., and Hou, Y. (2024). Morpheus: Modeling role from personalized dialogue history by exploring and utilizing latent space. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 7664–7676.
Wang, Y. (2024). Iterative feedback-enhanced prompting: A green algorithm for reducing household food waste. In 2024 International Conference on Machine Learning and Applications (ICMLA), pages 1–4. IEEE.
Wang, Y. (2025). Personalized llm response generation system for reducing household food waste. In 2025 IEEE Conference on Technologies for Sustainability (SusTech), pages 1–7. IEEE.
Wang, Z., Li, Z., Jiang, Z., Tu, D., and Shi, W. (2024). Crafting personalized agents through retrieval-augmented generation on editable memory graphs. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4891–4906.
Wasilewski, A. (2025). Harnessing generative ai for personalized e-commerce product descriptions: A framework and practical insights. Computer Standards & Interfaces, 94:104012.
Xie, H., Chen, Y., Xing, X., Lin, J., and Xu, X. (2025). Psydt: Using llms to construct the digital twin of psychological counselor with personalized counseling style for psychological counseling. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1081–1115.
Xu, H., Liu, H., Lv, Z., Yang, Q., and Wang, W. (2023). Pre-trained personalized review summarization with effective salience estimation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10743–10754.
Zhan, H., Lin, X., Cui, S., Zhao, Z., Zhou, W., and Chen, H. (2023). Towards zero-shot personalized table-to-text generation with contrastive persona distillation. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE.
Zhang, J. (2024). Guided profile generation improves personalization with large language models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 4005–4016.
Zhang, J., Liu, Y., Wang, W., Liu, Q., Wu, S., Wang, L., and Chua, T.-S. (2025a). Personalized text generation with contrastive activation steering. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7128–7141.
Zhang, L., Wu, J., Zhou, D., and He, Y. (2025b). Proper: A progressive learning framework for personalized large language models with group-level adaptation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16399–16411.
Zhang, Y., He, Y., and Zhou, D. (2025c). Rehearse with user: Personalized opinion summarization via role-playing based on large language models. In Findings of the Association for Computational Linguistics: ACL 2025, pages 15194–15211.
Zhang, Y., Yu, Z., Jiang, W., Shen, Y., and Li, J. (2023). Long-term memory for large language models through topic-based vector database. In 2023 International Conference on Asian Language Processing (IALP), pages 258–264. IEEE.
Balepur, N., Padmakumar, V., Yang, F., Feng, S., Rudinger, R., and Boyd-Graber, J. L. (2025). Whose boat does it float? improving personalization in preference tuning via inferred user personas. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3371–3393.
Dai, Z., Yao, C., Han, W., Yuanying, Y., Gao, Z., and Chen, J. (2024). Mpcoder: Multi-user personalized code generator with explicit and implicit style representation learning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3765–3780.
Fendji, J. L. K., Donatien, D., and Atemkeng, M. (2025). Hybrid profile based multi-document text summarisation. Procedia Computer Science, 252:862–872.
Gui, H. and Wang, Z. (2024). Response generation with personal attributes and act information. In 2024 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE.
Gupta, A., Hussain, M., Nikhileshwar, K., Rastogi, A., and Rangarajan, K. (2025). Integrating large language models into radiology workflow: Impact of generating personalized report templates from summary. European Journal of Radiology, 189:112198.
Han, A., Koushik, N., Bidarkundi, N., Shehata, M. N., Kunchipudi, V., Mammadli, T., Mehta, S., and Lerner, O. (2024). Enhancing llm conversational acuity using pragmatic measures. In 2024 15th International Conference on Information and Communication Technology Convergence (ICTC), pages 886–891. IEEE.
Hang, C. N., Tan, C. W., and Yu, P.-D. (2024). Mcqgen: A large language model-driven mcq generator for personalized learning. IEEE Access, 12:102261–102273.
Hu, Z., Zhao, H., Zhao, Y., Xu, S., and Xu, B. (2024). T-agent: A term-aware agent for medical dialogue generation. In 2024 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE.
Huang, B.-S., Kuo, T. T., Wang, L.-J., and Lin, C.-Y. (2025). Remind: Recall enhanced memory integration for natural language dialogue systems. In 2025 IEEE Conference on Artificial Intelligence (CAI), pages 1–6. IEEE.
Huang, Q., Liu, X., Ko, T., Wu, B., Wang, W., Zhang, Y., and Tang, L. (2024). Selective prompting tuning for personalized conversations with llms. In Findings of the Association for Computational Linguistics: ACL 2024, pages 16212–16226.
Huq, F., Samee, A., Lin, D. C.-E., Tang, A. X., and Bigham, J. P. (2025). Noteeline: Supporting real-time, personalized notetaking with llm-enhanced micronotes. In Proceedings of the 30th International Conference on Intelligent User Interfaces, pages 1064–1081.
Ji, B. (2023). Based on text augmentation personalized dialog generation with persona-sparse data. In 2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), pages 717–720. IEEE.
Ji, K., Lian, Y., Li, L., Gao, J., Li, W., and Dai, B. (2025). Enhancing persona consistency for llms’ role-playing using persona-aware contrastive learning. In Findings of the Association for Computational Linguistics: ACL 2025, pages 26221–26238.
Joko, H., Chatterjee, S., Ramsay, A., De Vries, A. P., Dalton, J., and Hasibi, F. (2024). Doing personal laps: Llm-augmented dialogue construction for personalized multi-session conversational search. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 796–806.
Ju, S. and Wang, C. (2024). Beyond simple text style transfer: Unveiling compound text style transfer with prompt-based pre-trained language models. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6850–6854. IEEE.
Ke, S.-W. and Chen, W.-L. (2021). Stylized dialogue generation. In 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pages 1456–1460. IEEE.
Kim, T., Agarwal, D., Ackerman, J., and Saha, M. (2025). Steering ai-driven personalization of scientific text for general audiences. Proceedings of the ACM on Human-Computer Interaction, 9(7):1–28.
Kirstein, F., Ruas, T., Kratel, R., and Gipp, B. (2024). Tell me what i need to know: Exploring llm-based (personalized) abstractive multi-source meeting summarization. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 920–939.
Kitchenham, B. et al. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1–26.
Lee, G., Jeong, M., Kim, Y., Jung, H., Oh, J., Kim, S., and Yun, S.-Y. (2024a). Bapo: Base-anchored preference optimization for overcoming forgetting in large language models personalization. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 6804–6820.
Lee, G. H., Lee, K. J., Jeong, B., and Kim, T. (2024b). Developing personalized marketing service using generative ai. Ieee Access, 12:22394–22402.
Li, C., Zhang, M., Mei, Q., Kong, W., and Bendersky, M. (2024). Learning to rewrite prompts for personalized text generation. In Proceedings of the ACM Web Conference 2024, pages 3367–3378.
Lian, J., Ao, X., Liu, X., Liu, Y., and He, Q. (2025). Panoramic interests: Stylistic-content aware personalized headline generation. In Companion Proceedings of the ACM on Web Conference 2025, pages 1109–1112.
Liu, D., Wu, Z., Song, D., and Huang, H.-Y. (2025a). A persona-aware llm-enhanced framework for multi-session personalized dialogue generation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 103–123.
Liu, J., Qiu, Z., Li, Z., Dai, Q., Yu, W., Zhu, J., Hu, M., Yang, M., Chua, T.-S., and King, I. (2025b). A survey of personalized large language models: Progress and future directions. arXiv preprint arXiv:2502.11528.
Liu, J., Zhu, Y., Wang, S., Wei, X., Min, E., Lu, Y., Wang, S., Yin, D., and Dou, Z. (2025c). Llms+ persona-plug= personalized llms. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9373–9385.
Neupane, S., Mitra, S., Mittal, S., Gaur, M., Golilarz, N. A., Rahimi, S., and Amirlatifi, A. (2025). Medinsight: A multi-source context augmentation framework for generating patient-centric medical responses using large language models. ACM Transactions on Computing for Healthcare, 6(2):1–19.
Nezhad, M. M. and Kangavari, M. (2024). Personalized persuasive text generation. In 2024 10th International Conference on Artificial Intelligence and Robotics (QICAR), pages 261–267. IEEE.
Nezhad, M. M., Kisomi, M. A., and Gholinezhad, F. (2025). Adaptive persuasion in conversational ai: An llm-driven framework for dynamic strategy switching via personality and sentiment analysis. In 2025 11th International Conference on Web Research (ICWR), pages 145–149. IEEE.
Oh, M., Lee, S., and Ok, J. (2025). Comparison-based active preference learning for multi-dimensional personalization. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 33145–33166.
Padmakumar, V., Wang, Z., Arbour, D., and Healey, J. (2025). Principled content selection to generate diverse and personalized multi-document summaries. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 29884–29899.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021). The prisma 2020 statement: an updated guideline for reporting systematic reviews. bmj, 372.
Prahlad, D., Lee, C., Kim, D., and Kim, H. (2025). Personalizing large language models using retrieval augmented generation and knowledge graph. In Companion Proceedings of the ACM on Web Conference 2025, pages 1259–1263.
Qiu, Y., Zhao, X., Zhang, Y., Bai, Y., Wang, W., Cheng, H., Feng, F., and Chua, T.-S. (2025). Measuring what makes you unique: Difference-aware user modeling for enhancing llm personalization. In Findings of the Association for Computational Linguistics: ACL 2025, pages 21258–21277.
Ryan, M. J., Shaikh, O., Bhagirath, A., Frees, D., Held, W. B., and Yang, D. (2025). Synthesizeme! inducing persona-guided prompts for personalized reward models in llms. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8045–8078.
Salemi, A., Kallumadi, S., and Zamani, H. (2024a). Optimization methods for personalizing large language models through retrieval augmentation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 752–762.
Salemi, A., Mysore, S., Bendersky, M., and Zamani, H. (2024b). Lamp: When large language models meet personalization. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7370–7392.
Shafi, F. R., Hossain, M. A., and Choudhury, S. (2025). Personalized mental health assistance with large language models. In 2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC), pages 815–823. IEEE.
Srivastava, H., Sunil, S., and Kumari, K. S. (2022). Neural text style transfer with custom language styles for personalized communication systems. In 2022 International Conference on Knowledge Engineering and Communication Systems (ICKES), pages 1–6. IEEE.
Tan, Z., Liu, Z., and Jiang, M. (2024a). Personalized pieces: Efficient personalized large language models through collaborative efforts. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 6459–6475.
Tan, Z., Zeng, Q., Tian, Y., Liu, Z., Yin, B., and Jiang, M. (2024b). Democratizing large language models via personalized parameter-efficient fine-tuning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 6476–6491.
Tang, Y., Wang, B., Zhao, D., Jinxiaojia, J., Zhangjijun, Z., He, R., and Hou, Y. (2024). Morpheus: Modeling role from personalized dialogue history by exploring and utilizing latent space. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 7664–7676.
Wang, Y. (2024). Iterative feedback-enhanced prompting: A green algorithm for reducing household food waste. In 2024 International Conference on Machine Learning and Applications (ICMLA), pages 1–4. IEEE.
Wang, Y. (2025). Personalized llm response generation system for reducing household food waste. In 2025 IEEE Conference on Technologies for Sustainability (SusTech), pages 1–7. IEEE.
Wang, Z., Li, Z., Jiang, Z., Tu, D., and Shi, W. (2024). Crafting personalized agents through retrieval-augmented generation on editable memory graphs. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4891–4906.
Wasilewski, A. (2025). Harnessing generative ai for personalized e-commerce product descriptions: A framework and practical insights. Computer Standards & Interfaces, 94:104012.
Xie, H., Chen, Y., Xing, X., Lin, J., and Xu, X. (2025). Psydt: Using llms to construct the digital twin of psychological counselor with personalized counseling style for psychological counseling. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1081–1115.
Xu, H., Liu, H., Lv, Z., Yang, Q., and Wang, W. (2023). Pre-trained personalized review summarization with effective salience estimation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10743–10754.
Zhan, H., Lin, X., Cui, S., Zhao, Z., Zhou, W., and Chen, H. (2023). Towards zero-shot personalized table-to-text generation with contrastive persona distillation. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE.
Zhang, J. (2024). Guided profile generation improves personalization with large language models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 4005–4016.
Zhang, J., Liu, Y., Wang, W., Liu, Q., Wu, S., Wang, L., and Chua, T.-S. (2025a). Personalized text generation with contrastive activation steering. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7128–7141.
Zhang, L., Wu, J., Zhou, D., and He, Y. (2025b). Proper: A progressive learning framework for personalized large language models with group-level adaptation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16399–16411.
Zhang, Y., He, Y., and Zhou, D. (2025c). Rehearse with user: Personalized opinion summarization via role-playing based on large language models. In Findings of the Association for Computational Linguistics: ACL 2025, pages 15194–15211.
Zhang, Y., Yu, Z., Jiang, W., Shen, Y., and Li, J. (2023). Long-term memory for large language models through topic-based vector database. In 2023 International Conference on Asian Language Processing (IALP), pages 258–264. IEEE.
Published
2026-06-01
How to Cite
CARAÍBA, André Felipe dos S.; OLIVEIRA, Sávio S. T. de; FERNANDES, Deborah S. A..
Personalized Text Generation with LLMs: A Systematic Literature Review with a Focus on Healthcare. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 26. , 2026, Ouro Preto/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 1265-1276.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2026.21732.
