Synthetic Client-Therapist Dialogue Generator

• Synthetic client-therapist dialogue generators are computational systems that simulate psychotherapy sessions using LLM-based client and therapist agents driven by detailed persona and scenario templates.
• They employ multi-stage pipelines including persona specification, dialogue orchestration, post-processing, and rigorous evaluation to ensure protocol adherence and data diversity.
• These systems enable scalable, privacy-preserving mental health research by augmenting training data and benchmarking therapeutic conversational skills in controlled settings.

A synthetic client-therapist dialogue generator is a computational system that produces artificial conversations simulating psychotherapy, typically by orchestrating the interaction between a "client" and a "therapist" agent—each realized by a LLM or transformer-based dialogue system—conditioned on explicit persona, clinical, and scenario representations. Such generators are foundational for advancing conversational AI in mental health, enabling large-scale data augmentation, controlled evaluation of dialogue models, benchmarking therapeutic skills, and mitigating privacy concerns around real-world clinical data (Burdisso et al., 12 Jun 2025).

1. Core Architecture and Workflow Components

The synthetic client-therapist dialogue generation pipeline in contemporary toolkits such as SDialog (Burdisso et al., 12 Jun 2025), DiaSynth (Suresh et al., 25 Sep 2024), and related frameworks can be decomposed into the following principal stages:

1. Persona Specification: Construction of rich speaker profiles with attributes such as age, gender, background, presenting issues, personality traits, and communication style. These are instantiated as formally structured objects or JSON-like schemas.
2. Scenario and Technique Template: Definition of the therapeutic context—such as session goals (e.g., "reframe negative thoughts"), targeted modalities (e.g., CBT, MI), and scenario-specific prompts or technique constraints.
3. Dialogue Orchestration and Simulation: Multi-agent interaction is managed via orchestrators that enforce turn logic, inject scenario-driven reminders, constrain session length, and control diversity of utterances. LLM-driven simulations employ instruction-tuned agents (e.g., PersonaAgent) seeded with the explicit profile and scenario state.
4. Post-processing and Filtering: Output dialogues undergo automated filtering for minimal length, presence of key therapeutic moves, de-duplication by semantic similarity, and annotation with dialogue acts (e.g., QUESTION, REFLECT).
5. Evaluation and Logging: Generated corpora are evaluated by both automatic metrics (perplexity, distinct-n, embedding-based coherence) and human-labeled criteria such as empathy, coherence, and domain appropriateness, with full metadata logging for reproducibility (Burdisso et al., 12 Jun 2025, Suresh et al., 25 Sep 2024, Vu et al., 29 Oct 2025).

This pipeline is highly modular—enabling plug-and-play of profiles, scenario templates, and orchestration logic to standardize synthetic data generation across different therapeutic modalities and use-cases.

2. Persona Conditioning and Scenario Design

Client and therapist personas are constructed from a formal schema capturing both demographic and clinical attributes:

Field	Example: Client	Example: Therapist
ID	"P01"	"T13"
Role	"client"	"therapist"
Age, Gender	28, "female"	45, "male"
Profession	"graphic designer"	"clinical psychologist"
Presenting Issues	["work stress"]	N/A
Personality Traits	["anxious", "introvert"]	["warm", "reflective"]
Communication	{"formality": "medium"}	{"formality": "high"}

These representations are injected into LLM prompts (either as serialized text blocks or as explicit variable values) to induce role fidelity and ensure speaker utterances conform to the required clinical and stylistic attributes (Suresh et al., 25 Sep 2024, Burdisso et al., 12 Jun 2025). Scenario templates encode details such as targeted cognitive distortions, use of Socratic questioning, session structure, and initial prompts—supporting both free-form and strictly protocol-driven simulation (Wasenmüller et al., 13 Dec 2024).

3. Dialogue Generation Methodologies

A variety of methodologies underpin synthetic client-therapist dialogue generation:

• Instruction-Tuned LLM Agents: Agents (e.g., PersonaAgent in SDialog (Burdisso et al., 12 Jun 2025)) run LLMs parameterized by persona profiles, with orchestration layers handling turn-taking, instructional cues, and optional diversity control. Dialogues can be simulated in multi-turn fashion with seeds for reproducibility.
• Chain-of-Thought (CoT) Prompting: DiaSynth and related frameworks structure the generation task using explicit CoT templates that require the LLM to plan dialogues step-by-step—specifying session context, emotion and action per turn, then surface realizations (Suresh et al., 25 Sep 2024).
• Script-Based Deterministic Policy Planning: In this approach, interaction is governed by an expert-written "script"—a finite-state machine with explicit goals, ordered tasks, transition functions, and branching rules per section. The LLM is constrained to only produce utterances compatible with the current state and prescribed tasks (Wasenmüller et al., 13 Dec 2024).
• Dual-Agent and Role-Play Simulation: Systems like SQPsych and SimPsyDial instantiate separate client and therapist agents, each with individualized prompt templates, operating in strictly alternating turn-taking mode (Vu et al., 29 Oct 2025, Qiu et al., 28 Aug 2024). Such dual-agent setups enable fine-grained modeling of interactional dynamics and role fidelity.
• Conversational Self-Play: Both agent roles are realized by LLMs in alternating fashion, with session-level variation in client symptom profiles and therapy guidance prompts to generate diverse and modality-aligned corpora (Kampman et al., 17 Mar 2025).

Sampling strategies often include temperature, nucleus sampling, and beam search control to modulate output creativity and coherence. Post-generation filtering (deduplication, hallucination detection) ensures quality and diversity.

4. Mathematical Formulations and Evaluation Metrics

Dialogue generation adheres to standard sequence modeling formalism, with the conditional probability of the next utterance $a_t$ given the historical turns $h_t$ modeled as:

$$p(a_t | h_t) = \mathrm{softmax}( f_\theta(h_t, a_t) )$$

where $f_\theta$ denotes the un-normalized LLM logits (Burdisso et al., 12 Jun 2025).

Empathy and alignment are scored with composite functions, e.g.:

$$\mathrm{Emp}(u_t) = w_1 I_{\text{reflect}}(u_t) + w_2 I_{\text{valid}}(u_t) + w_3 \,\mathrm{sentiment\_match}(u_t, h_t)$$

where each indicator corresponds to the presence of reflection/validation and the sentiment embedding match between turns (Burdisso et al., 12 Jun 2025).

Diversity and coherence are respectively assessed with:

• $$\mathrm{Distinct}\text{-}n = \frac{\# \text{ unique } n\text{-grams}}{\# \text{ total } n\text{-grams}}$$
• $$\mathrm{Coh} = \frac{1}{T-1} \sum_{t=2}^T \cos(\mathrm{emb}(u_t), \mathrm{emb}(u_{t-1}))$$

Task-specific metrics include perplexity on held-out real data, intent and act coverage (e.g., CBT move execution), and clinical benchmarks such as CounselingBench, CBT-Bench, F1, precision, and recall on benchmarked subtasks (Vu et al., 29 Oct 2025). For protocol-constrained therapies, session-level structural fidelity is further measured by speaker switch ratio, protocol step completion rates, and SUDS monitoring frequency (BN et al., 30 Apr 2025).

Human evaluations typically have expert raters assess empathy, coherence, usefulness, and clinical fidelity on Likert-type scales, with inter-annotator agreement statistics such as Krippendorff’s $\alpha$ or Cohen’s $\kappa$ (Burdisso et al., 12 Jun 2025, Vu et al., 29 Oct 2025). LLM-based judges are increasingly used for scalable assessment of CBT criteria and alliance quality (Vu et al., 29 Oct 2025, Qiu et al., 28 Aug 2024).

5. Protocols for Realism, Reproducibility, and Modality Coverage

Ensuring realism, faithfulness to clinical technique, and reproducibility entails:

• Explicit logging of all generation hyperparameters (e.g., temperature, top-k, prompt templates, seeds), with deterministic pseudorandom sequence control to facilitate exact experiment replication.
• Post hoc filtering of dialogues to exclude those falling below turn or therapeutic act thresholds.
• Automated annotation for dialogue acts, empathy moves, and protocol steps, supporting downstream auditability.
• Best practices recommend early and continuous feedback from licensed clinicians to calibrate metrics against domain expertise, and versioning all synthetic samples with parameter provenance (Burdisso et al., 12 Jun 2025, Suresh et al., 25 Sep 2024).

Coverage of distinct psychotherapeutic modalities (CBT, MI, PE, PCT, psychodynamic) is achieved by composable scenario templates, script content, and prompt engineering. For fine-grained fidelity, scripts or templates are adjusted at the task and transition level to enforce session structure and therapeutic alignment (Wasenmüller et al., 13 Dec 2024, BN et al., 30 Apr 2025).

6. Representative Dialogue Excerpts and Corpus Characteristics

Typical synthetic sessions exhibit alternation of open-ended questioning, validation, empathy, and evidence-based intervention, consistent with CBT and motivational interviewing practices (Burdisso et al., 12 Jun 2025, Vu et al., 29 Oct 2025). In the case of PE therapy, session structure and protocol adherence—for example, correct SUDS check frequency and usage of exposure rationale—are critical fidelity benchmarks (BN et al., 30 Apr 2025).

Corpus statistics reported include mean turns per session (∼17–24), tokens per utterance (26–51), and coverage of required protocol markers (e.g., distress monitoring, cognitive restructuring frequency) (Vu et al., 29 Oct 2025, BN et al., 30 Apr 2025). Comparative evaluations show that, although structural features (e.g., speaker switch ratio) are closely matched between real and synthetic dialogues, specific clinical act frequencies and subtle interactional nuances may diverge (BN et al., 30 Apr 2025, Qiu et al., 28 Aug 2024).

7. Limitations, Pitfalls, and Future Directions

Current systems are subject to a range of limitations:

• Incomplete coverage of the full spectrum of disorders (often limited to depression, anxiety) and single-session or text-only constraints.
• Inadequate modeling of session progression, client resistance, and rupture/repair phenomena.
• Occasional protocol failures—such as missing fidelity markers, hallucinations, or superficial dialogue drift away from clinical standards.
• Disagreement between automated and human expert evaluations (e.g., only moderate correlation, $ρ \approx 0.40$ in LLM-judge and human ratings (Vu et al., 29 Oct 2025)).

Recommended future work includes:

• Multi-task and protocol-adherence objectives in model training.
• Explicit classifier heads for turn-level fidelity signals (e.g., SUDS checks).
• Longer-session, multi-session, and multimodal (audio/nonverbal) enhancements.
• Expanded evaluation frameworks incorporating both clinical fidelity and user-centered conversational usability metrics.
• Continuous iteration of self-play and script-based architectures, with the integration of reinforcement learning from therapist or client reward signals (Kampman et al., 17 Mar 2025, Wasenmüller et al., 13 Dec 2024).

Synthetic client-therapist dialogue generators—grounded in the architectures, methodologies, and evaluation practices outlined above—provide a reproducible, scalable, and privacy-preserving backbone for contemporary research and development in conversational AI for mental health (Burdisso et al., 12 Jun 2025, Suresh et al., 25 Sep 2024, Wasenmüller et al., 13 Dec 2024, Vu et al., 29 Oct 2025, Qiu et al., 28 Aug 2024, BN et al., 30 Apr 2025, Kampman et al., 17 Mar 2025).