Domain Classification & Persona Assignment

• Domain classification and persona assignment are techniques that identify and encode roles and behavioral attributes from text and data to improve systems like dialogue agents and recommender systems.
• Neural and memory-augmented architectures combine bidirectional GRUs with multi-level attention and external knowledge to extract comprehensive persona embeddings for robust classification.
• Graph-based approaches and meticulous dataset annotation enable multi-contextual persona embeddings, improving link prediction and node classification accuracy while addressing bias and ethical considerations.

Domain classification and persona assignment are intertwined processes enabling artificial intelligence systems to detect, categorize, and generate actionable representations of roles, identities, or behavioral attributes from text, dialogue, or structured data. These methodologies have evolved to support personalized dialogue agents, narrative analysis, user segmentation, and targeted information delivery across domains such as conversational AI, recommender systems, and social computing. The central challenge is capturing the underlying persona—be it a character trope, user role, or professional archetype—while simultaneously discerning or leveraging the target domain to inform downstream outputs or behaviors.

1. Neural and Memory-Augmented Architectures for Persona Assignment

Attentive Memory Networks exemplify advanced architectures for persona extraction from dialogue, as demonstrated in film character trope classification (Chu et al., 2018). The system encodes dialogue snippets comprising self (D), contextual (E), and other (O) utterances using bidirectional GRUs with word-level self-attention to weigh personality-revealing content. A multi-level attention mechanism is then introduced: first aggregating over individual dialogues, followed by weighting across snippets to extract a comprehensive persona embedding $$z = \gamma_D^s D^s + \gamma_E^s E^s + \gamma_O^s O^s$$ where parameters $\gamma^s$ are learned to balance the influence of each component.

Prior knowledge (e.g., trope descriptions) is incorporated through a memory module initialized with skip-thought vector embeddings. The persona embedding is projected into this knowledge space, facilitating a form of memory-augmented reasoning where persona–trope similarity guides both classification and embedding refinement.

These methods collectively enable models to robustly extract, categorize, and compare personas across narrative domains or short-text environments with strong empirical advantages in classification metrics over single-snippet or non-attentive baselines.

2. Graph-Based Approaches and Multi-Persona Embeddings

The advent of PersonaSAGE and related architectures extends persona assignment into graphs with multi-contextual roles (Choudhary et al., 2022), critical for domain classification in networks or recommendation settings. Instead of a single node embedding, PersonaSAGE learns multiple context-specific “persona” embeddings per node, reflecting the diverse roles a node (e.g., a user or paper) occupies.

Initialization uses clustering (e.g., KMeans) over features to assign initial persona memberships; during message passing, these memberships and associated embeddings are iteratively refined. The system dynamically determines the number of personas per node by propagating membership probabilities across the graph and updating only those embeddings with nonzero membership. This allows the framework to disambiguate polysemous nodes, assign domain roles, and enable fine-grained classification—shown to improve link prediction AUC by 6–15% and node classification accuracy by up to 17% over single-embedding GNNs.

3. Dataset Construction and Annotation Strategies

Crowdsourced and human-annotated datasets remain foundational for persona and domain ground truth. Large-scale dialogue corpora, such as those constructed with detailed persona profiles and controlled crowdworker-user interaction protocols (Cho et al., 2023), employ pre-interviews and strict guidelines for persona consistency, empathy, and disclosure. Artificial durations and real-time moderation are instituted to manage conversational naturalness and participant well-being.

Domain classification is subsequently performed with tokenization, stopword removal, and embedding (e.g., Word2Vec), followed by unsupervised clustering (e.g., K-means, t-SNE visualization) to reveal underlying domain groups within the dialogue data. This approach ensures that collected corpora are meaningfully diverse in both persona instantiations and topic domains, directly supporting both persona assignment pipelines and downstream modeling.

4. Bias, Consistency, and Ethical Considerations in Persona Prompting

Research into persona prompting in LLMs exposes complex interplays between domain signals, persona attributes, and systematic biases. Empirical studies show that imposing personas based on socio-demographic or nationality cues (e.g., “Black person,” “physically-disabled,” “American person,” etc.) can trigger performance drops of 30–70% on reasoning tasks due to latent stereotypes or abstention behavior (Gupta et al., 2023, Kamruzzaman et al., 20 Jun 2024, Araujo et al., 2 Jul 2024). These effects are robust across models, datasets, and even when explicit debiasing prompts are applied.

Nationality-assigned personas cause LLMs to exaggerate ingroup favoritism and regional bias, while MBTI-personality-infused prompts produce measurable divergences in hate speech classification confidence (logit distributions) (Yuan et al., 10 Jun 2025). Structured frameworks use entropy to quantify intra- and inter-persona consistency, revealing that model outputs are more stable for explicit roles (happiness, occupation) and less so for political stance or latent personality (Reusens et al., 3 Jun 2025).

Mitigation strategies (e.g., persona-instruction or refinement layers) offer partial improvement for robustly handling irrelevant persona details but only scale effectively on the largest foundation models (Araujo et al., 27 Aug 2025). The field calls for explicit robustness checks and principled persona prompt design emphasizing only domain-relevant traits, guided by formal desiderata such as Expertise Advantage, Robustness, and Fidelity.

5. Knowledge Integration, Domain Transfer, and Evaluation

State-of-the-art models merge structured knowledge with learning-based extraction. For instance, integrating textual descriptions (domain ontologies, trope lists) as read-only memories or keys in modular neural architectures enhances both flexibility and performance in new domains (Chu et al., 2018). For dynamic or out-of-domain persona extraction, NLI-based post-processing validates whether candidate persona triplets are entailed by the dialogue, sharply reducing hallucination when transferring from real-world to narrative domains (e.g., the LIGHT dataset) (DeLucia et al., 12 Jan 2024).

Benchmarks such as PersonaFeedback decouple persona inference from output personalization by explicitly providing rich, human-crafted personas and demanding that models generate or select responses aligned with those profiles (Tao et al., 15 Jun 2025). Evaluation strategies employ binary choice, Fleiss’s Kappa, and human–AI critiquing concordance ($\kappa = 0.893$). Performance, especially in differentiating nuanced (hard-tier) personalization, remains challenging even for state-of-the-art LLMs.

6. Applications, Limitations, and Future Directions

Persona assignment and domain classification underpin a spectrum of practical systems:

• Narrative and character analysis: inferring archetypes or clustering characters/movies for computational humanities or content recommendation (Chu et al., 2018).
• Dialogue agents and chatbots: driving natural, context-aware interaction by assigning and leveraging detailed persona embeddings for response generation, empathy, and memory (Cho et al., 2023, Zaitsev, 17 Dec 2024).
• Recommender systems: customer personas derived from purchase histories yield interpretable user representations that enhance segmentation and boost NDCG@K and F1@K by up to 12% (Shi et al., 24 Apr 2025).
• Evaluation: frameworks such as PersonaGym and PersonaScore assess persona fidelity in a dynamic, decision-theoretic context, establishing that model scale is not alone sufficient for improved persona adherence (Samuel et al., 25 Jul 2024).

Technical and scientific challenges include ensuring robust, bias-mitigated persona handling across domains, scalable annotation and data curation, continual learning for dynamic behavior, and integrating explainability (e.g., via SHAP) for trust and accountability (Afzoon et al., 21 Aug 2025).

A plausible implication is that the success of future systems will depend on the integrated design of persona representations, attention to domain signals, and principled evaluation metrics. The use of hybrid models, memory modules, and graph-based propagation offers promising avenues for simultaneously advancing classification accuracy, personalization, and transparency.