Dialogue Management for Coherence
Posted: Wed May 21, 2025 5:46 am
Large Language Models (LLMs): Fine-tuning pre-trained LLMs (like GPT-3.5, GPT-4, or open-source alternatives) on your specific Telegram chat histories. This allows the model to learn your brand's unique tone, vocabulary, and conversational style.
Supervised Fine-tuning: Providing input-output pairs (user message - desired persona response).
Reinforcement Learning from Human Feedback (RLHF): Humans rate bot responses for helpfulness, harmlessness, and adherence to the desired persona, further refining the model.
Conditional Text Generation: Training models to generate responses that are conditional on specific inputs (e.g., sentiment, detected intent, or persona parameters).
Reinforcement Learning: Models learn optimal conversational turns based on past successful dialogues.
State Tracking: The AI keeps track of the conversation's usa telegram mobile phone number list context, previous turns, and user preferences to maintain coherence across interactions.
Persona Definition & Refinement:
Explicit Persona Attributes: Define key characteristics for your AI persona:
Tone: Friendly, formal, empathetic, humorous.
Vocabulary: Industry-specific jargon, simple language, informal slang.
Response Length: Concise, detailed.
Proactive vs. Reactive: Does it offer solutions before being asked?
Knowledge Domain: What topics is it authoritative on?
Human Feedback Loops:
Red Teaming: Actively trying to break the persona, get it to go "off-script," or exhibit undesirable behavior.
A/B Testing: Compare different persona variations in live environments (with consent).
User Surveys: Gather direct feedback on the AI's helpfulness, perceived personality, and conversational style.
Deployment & Integration:
Supervised Fine-tuning: Providing input-output pairs (user message - desired persona response).
Reinforcement Learning from Human Feedback (RLHF): Humans rate bot responses for helpfulness, harmlessness, and adherence to the desired persona, further refining the model.
Conditional Text Generation: Training models to generate responses that are conditional on specific inputs (e.g., sentiment, detected intent, or persona parameters).
Reinforcement Learning: Models learn optimal conversational turns based on past successful dialogues.
State Tracking: The AI keeps track of the conversation's usa telegram mobile phone number list context, previous turns, and user preferences to maintain coherence across interactions.
Persona Definition & Refinement:
Explicit Persona Attributes: Define key characteristics for your AI persona:
Tone: Friendly, formal, empathetic, humorous.
Vocabulary: Industry-specific jargon, simple language, informal slang.
Response Length: Concise, detailed.
Proactive vs. Reactive: Does it offer solutions before being asked?
Knowledge Domain: What topics is it authoritative on?
Human Feedback Loops:
Red Teaming: Actively trying to break the persona, get it to go "off-script," or exhibit undesirable behavior.
A/B Testing: Compare different persona variations in live environments (with consent).
User Surveys: Gather direct feedback on the AI's helpfulness, perceived personality, and conversational style.
Deployment & Integration: