Steering LLM Psychotherapy Interactions

# Illustrative sketch (not from the paper)
import torch
# Define the ten‑move therapy ontology (derived from MULTI‑60)
ONTOLOGY = ["Inquiry", "Reflection", "Affirmation", "Interpretation", "Exploration", "Summarization", "GoalSetting", "Psychoeducation", "EmotionLabeling", "Closure"]

# Placeholder LLM judge that returns the best‑matching move for a therapist turn
def classify_move(turn_text, judge_model="GLM-5.2"):
    # In practice this would call the LLM judge API; here we mock with a simple heuristic
    # e.g., look for keywords; replace with actual model call
    for move in ONTOLOGY:
        if move.lower() in turn_text.lower():
            return move
    return "Inquiry"  # default fallback

# Therapist model that receives the ontology as a set of tools
def therapist_generate(patient_input, therapist_model="GLM-5.2"):
    # Build a system prompt that lists allowed moves
    tool_prompt = f"Allowed therapeutic moves: {', '.join(ONTOLOGY)}."
    # Combine with patient input
    full_prompt = tool_prompt + "\nPatient: " + patient_input + "\nTherapist:"
    # Call the LLM (placeholder)
    response = torch.tensor([0])  # mock tensor representing generated text
    # In real code, replace with model.generate(...)
    return "Generated therapist turn based on ontology tools"

# Example usage
patient_utterance = "I feel anxious about my job."
move = classify_move(patient_utterance)
therapist_reply = therapist_generate(patient_utterance)
print(move, therapist_reply)

// Illustrative sketch (not from the paper)
const ONTOLOGY = ["Inquiry","Reflection","Affirmation","Interpretation","Exploration","Summarization","GoalSetting","Psychoeducation","EmotionLabeling","Closure"];

// Mock LLM judge that classifies a therapist turn
function classifyMove(turnText, judgeModel="GLM-5.2") {
  // Real implementation would call an LLM API
  for (const move of ONTOLOGY) {
    if (turnText.toLowerCase().includes(move.toLowerCase())) {
      return move;
    }
  }
  return "Inquiry"; // fallback
}

// Therapist generator that receives ontology as tools
function therapistGenerate(patientInput, therapistModel="GLM-5.2") {
  const toolPrompt = `Allowed therapeutic moves: ${ONTOLOGY.join(", ")}.`;
  const fullPrompt = `${toolPrompt}\nPatient: ${patientInput}\nTherapist:`;
  // Placeholder for model generation (e.g., using a library or API)
  const response = ""; // mock empty string
  return "Generated therapist turn based on ontology tools";
}

// Example usage
const patientUtterance = "I feel anxious about my job.";
const move = classifyMove(patientUtterance);
const therapistReply = therapistGenerate(patientUtterance);
console.log(move, therapistReply);