Beyond Generic Responses: Personalizing AI with User Profiles

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Author: Denis Avetisyan

New research demonstrates a prompting framework that infuses large language models with user-specific information to deliver more relevant and engaging responses.

The PARAN framework establishes a comprehensive approach to problem-solving, acknowledging that even the most innovative architectures inevitably contribute to future technical debt as production environments expose unforeseen limitations and complexities.
The PARAN framework establishes a comprehensive approach to problem-solving, acknowledging that even the most innovative architectures inevitably contribute to future technical debt as production environments expose unforeseen limitations and complexities.

This paper introduces PARAN, a persona-augmented system leveraging both explicit and implicit cues from food delivery reviews to enhance response personalization without requiring model fine-tuning.

While large language models excel at text generation, delivering truly personalized responses remains challenging, particularly when user data is scarce. This limitation motivates the development of ‘PARAN: Persona-Augmented Review ANswering system on Food Delivery Review Dataset’, a novel prompting framework designed to infer both explicit and implicit user personas directly from short food delivery reviews. By integrating these inferred attributes into response generation, PARAN enhances the relevance and personalization of automated replies without requiring costly model fine-tuning. Could this approach unlock more engaging and effective conversational experiences across a wider range of platforms with limited user profiles?

The Illusion of Personalization: Why LLMs Still Feel Generic

Contemporary Large Language Models, while impressive in their breadth of knowledge, frequently deliver responses that lack the specificity required for truly helpful interactions, particularly in practical applications like food delivery. A user requesting “something Italian” might receive a list of every Italian restaurant within range, rather than recommendations tailored to their past orders, dietary restrictions, or expressed preferences for spicy dishes. This tendency towards generic output stems from the models’ reliance on broad statistical patterns within their training data, hindering their ability to discern subtle cues and individual needs. Consequently, the experience can feel impersonal and require significant user effort to refine the results, highlighting a crucial limitation in current LLM capabilities and the need for more nuanced personalization strategies.

Truly personalized experiences necessitate a deep comprehension of user desires that extends beyond readily available information. Current systems often rely on keyword matching – if a user mentions “pizza,” they receive pizza recommendations – but human preferences are far more complex. Effective personalization demands inferring implicit preferences – perhaps a consistent avoidance of spicy foods, or a tendency towards vegetarian options – alongside explicitly stated choices. This requires models capable of building a nuanced user profile, recognizing patterns in behavior, and anticipating needs before they are directly expressed. Simply identifying keywords fails to capture the subtle factors influencing individual tastes and ultimately limits the potential for genuinely helpful and engaging interactions; a system must understand why a user makes certain choices, not just what those choices are.

Successfully integrating user preferences into large language model outputs presents a significant technical hurdle, demanding a delicate balance between customization and linguistic integrity. Simply appending preferred terms or topics can lead to disjointed, nonsensical text, or even introduce factual errors; the model must understand how preferences subtly shape appropriate responses. Researchers are actively exploring methods to guide text generation – such as reinforcement learning from human feedback and constrained decoding – that allow for the incorporation of individual tastes without compromising the overall coherence or veracity of the generated content. This requires developing algorithms capable of nuanced semantic control, ensuring that personalized details are woven seamlessly into the narrative fabric, rather than appearing as jarring additions.

Higher temperatures increase response diversity but decrease lexical precision, demonstrating a trade-off between these two qualities.
Higher temperatures increase response diversity but decrease lexical precision, demonstrating a trade-off between these two qualities.

PARAN: A Framework for Pretending to Know You

The PARAN framework constructs user personas by integrating two primary data sources: explicit and implicit cues. Explicit persona information encompasses directly stated user characteristics, such as documented preferences-for example, dietary restrictions, preferred communication channels, or stated interests-typically gathered from user profiles, reviews, or direct input. Complementing this, PARAN utilizes implicit cues derived from a user’s linguistic style, encompassing elements like vocabulary choice, sentence structure, and typical phrasing, analyzed from their written or spoken communications. This combination allows for a multifaceted understanding of the user beyond stated preferences, capturing nuances in communication style to inform response generation.

The PARAN framework utilizes persona information within LLM prompting strategies through the construction of targeted prompts that include explicit persona details and stylistic guidelines. These prompts are designed to condition the LLM’s output, directing it to generate responses that align with the identified user characteristics. Specifically, persona data informs the prompt’s phrasing, vocabulary, and overall tone, ensuring contextual relevance. The framework employs techniques such as few-shot learning, providing the LLM with examples of desired responses tailored to the specific persona, and utilizes persona-specific keywords to further refine the generated text. This approach allows for dynamic adaptation of the LLM’s output based on the individual user profile, moving beyond generalized responses to achieve a higher degree of personalization.

The PARAN framework addresses the common issue of Large Language Models (LLMs) producing generalized responses lacking individual nuance. Current LLMs often fail to tailor output to specific user characteristics, resulting in communication that, while grammatically correct, lacks a personalized touch. PARAN seeks to rectify this by incorporating both explicitly provided user data – such as stated preferences or demographic information – and implicitly derived traits, like linguistic patterns, into the LLM prompting process. This combined approach aims to move beyond standardized outputs and generate responses demonstrably aligned with individual user profiles, effectively creating a more engaging and relevant communication experience.

Validation: Numbers to Justify the Illusion

The PARAN framework’s performance was quantitatively assessed using established natural language generation metrics to evaluate both the semantic similarity and lexical characteristics of generated text. BLEU (Bilingual Evaluation Understudy) and METEOR measure content overlap between generated and reference texts, while Rouge-2 specifically assesses recall based on bigram co-occurrence. To quantify lexical diversity, Distinct-2 calculates the number of unique bigrams present in the generated output, indicating the range of vocabulary used. BERTScore, leveraging contextual embeddings from the BERT model, provides a more nuanced evaluation of semantic similarity by considering contextual information. These metrics, used in combination, provide a comprehensive understanding of PARAN’s impact on generated text quality.

The PARAN framework incorporated several large language models (LLMs) – specifically GPT-3.5 Turbo, Llama 3.1 (both 8B and 70B parameter versions), Claude 3.5 Haiku, and Amazon Nova Lite – to assess its performance across diverse architectures and capabilities. This multi-model approach was implemented to guarantee the robustness of PARAN’s improvements, ensuring that observed gains weren’t specific to a single LLM’s characteristics or limitations. Evaluating PARAN with models varying in size and training data helps to validate its general applicability and reliability in enhancing LLM-generated content, irrespective of the underlying model used.

Evaluation of the PARAN framework demonstrates consistent improvements in LLM-generated response quality across multiple models. Utilizing only the implicit persona, PARAN achieved a maximum 35.9% gain in Distinct-2 with the Claude 3.5 Haiku model. Specific gains observed with Claude 3.5 Haiku using only the explicit persona included 0.2521 Rouge-2, 0.3241 BLEU, and 0.1309 METEOR. With GPT-3.5 Turbo, utilizing only the explicit persona resulted in improvements of 0.0657 Rouge-2, 0.0332 BLEU, and 0.2306 METEOR. Furthermore, a BERTScore F1 score of 0.0802 was achieved using the GPT-4o mini model.

Semantic robustness decreases with increasing temperature, indicating the model becomes more susceptible to prompt perturbations at higher temperatures.
Semantic robustness decreases with increasing temperature, indicating the model becomes more susceptible to prompt perturbations at higher temperatures.

Beyond the Hype: Where This All Might Actually Matter

The potential of the PARAN framework extends far beyond optimizing food delivery experiences, representing a versatile approach to personalized communication applicable across diverse sectors. Industries reliant on effective interaction – including customer service, where tailored responses can significantly improve satisfaction, education, where learning can be adapted to individual student needs, and healthcare, where empathetic and clear communication is paramount – stand to benefit from this technology. By moving beyond generic interactions, PARAN facilitates the creation of communication strategies uniquely suited to each user, fostering stronger relationships and achieving more favorable outcomes in any domain where personalization is key to success.

The PARAN framework demonstrably enhances interactions by tailoring communication to individual user preferences and stylistic cues. This personalization moves beyond simple information delivery, fostering a sense of connection and understanding that directly impacts user engagement. Studies reveal that when large language models adapt to a user’s preferred tone – be it formal, casual, or humorous – satisfaction levels significantly increase. Moreover, aligning the communication style with established user patterns improves comprehension and recall, ultimately leading to more effective outcomes in diverse applications, from personalized learning experiences to nuanced customer support scenarios. The ability to mirror a user’s linguistic fingerprint not only makes interactions more pleasant, but also builds trust and encourages continued engagement with the system.

Ongoing research centers on bolstering the accuracy of persona inference – the process of discerning individual user characteristics from limited data – and devising novel methods to seamlessly incorporate these inferred personas into Large Language Model (LLM) prompting. This includes investigating techniques beyond simple stylistic adaptation, such as dynamically adjusting the LLM’s knowledge base or reasoning strategies to align with a user’s established preferences and cognitive style. The aim is not merely to generate text in the style of a user, but to create interactions that are genuinely tailored to their individual needs and expectations, potentially enhancing comprehension, trust, and overall satisfaction. Future iterations will also explore the robustness of these techniques across diverse datasets and user demographics, ensuring equitable performance and minimizing the risk of unintended biases.

The pursuit of perfectly personalized responses feels… familiar. This PARAN framework, attempting to inject persona directly into prompts, assumes a level of consistency in user expression that rarely exists. The bug tracker will inevitably fill with examples of implicit persona failing to translate, of nuanced sentiment lost in extraction. It’s a noble attempt to avoid fine-tuning-expensive and brittle as that is-but it feels like building a house of cards. Vinton Cerf observed, “Any sufficiently advanced technology is indistinguishable from magic.” Perhaps that’s the point. This isn’t about building robust systems; it’s about creating the illusion of understanding, and hoping production doesn’t notice the seams. The core idea of leveraging implicit persona is sound, but the reality will be messier. They don’t deploy – they let go.

What’s Next?

The pursuit of persona-driven response generation, as exemplified by PARAN, inevitably bumps against the inherent messiness of production data. While prompt engineering offers a seemingly elegant solution – sidestepping the resource demands of full model retraining – it merely delays the inevitable. Every abstraction dies in production, and the carefully constructed personas will, in time, encounter reviews that defy categorization, or users who intentionally subvert the system. The system’s reliance on extracted persona traits introduces a fragility; the quality of the generated response is fundamentally limited by the accuracy of that initial extraction.

Future work will likely focus on increasingly sophisticated methods for handling ambiguity and contradiction in user-expressed persona. However, a more fundamental challenge remains: the assumption that a static, extracted persona can adequately represent the fluid, contextual nature of human expression. The system might benefit from incorporating mechanisms for dynamic persona adaptation – inferring intent and adjusting responses on a per-utterance basis – but this introduces further complexity and potential for instability.

Ultimately, this line of inquiry is a refinement of existing techniques, not a revolution. The goalposts simply shift; what constitutes a ‘personalized’ response will continuously evolve, driven by user expectation and the ever-present need to mitigate adversarial input. Everything deployable will eventually crash, and PARAN, however refined, will be no exception. At least it dies beautifully.

Original article: https://arxiv.org/pdf/2512.10148.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-14 23:20