XO GPT: Response Rephrasing Model

Back to XO GPT Model Specifications

The XO GPT Response Rephrasing model enhances the quality and naturalness of AI Agent interactions. It rephrases predefined responses to preserve conversation context, adapt to user emotion, and produce more empathetic, human-like replies—improving user satisfaction without changing the underlying content.

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Challenges with Commercial Models

Challenge	Impact
Latency	High processing times affect user experience in real-time or high-volume scenarios.
Cost	Per-request pricing scales poorly for large deployments.
Data Governance	Sending conversations to external models raises privacy and security concerns.
Lack of Customization	General-purpose models are not tuned for specific industries or use cases.
Limited Control	Minimal ability to correct or refine model behavior for incorrect outputs.
Compliance Constraints	Some industries have regulatory requirements that commercial LLM providers don’t fully support.

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Key Assumptions

• Designed for text-based conversations only.
• Rephrases AI Agent responses only; does not modify user inputs.

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Benefits

Empathetic and Contextual Communication

Tailors responses to user sentiment and tone for engaging, empathetic interactions. See Model Benchmarks for performance metrics.

Cost-Effective

For Enterprise Tier customers, XO GPT eliminates commercial model usage costs. Example comparison (120 input tokens/conversation, 10,000 daily interactions, 40 tokens/response):

Model	Input $/MTok	Output $/MTok	Input $/Year	Output $/Year	Total $/Year
GPT-4 Turbo	$30	$60	$13,140	$8,760	$21,900
GPT-4	$10	$30	$4,380	$4,380	$8,760
GPT-4o Mini	$0.15	$0.60	$65.70	$87.60	$153.30

Enhanced Security

No client or user data is used for model retraining.

Guardrails: Content moderation, behavioral guidelines, response oversight, input validation, and usage controls.

AI Safety: Ethical guidelines, bias monitoring, transparency, and continuous improvement.

Performance, features, and language support may vary by implementation. Test thoroughly in your environment before production use.

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Use Cases

Domain	Use Cases
Customer Support	Rephrasing responses to sound empathetic during complaints; converting technical jargon to plain language; rephrasing policy updates reassuringly
Healthcare	Rephrasing medical instructions for clarity and compassion; responding to sensitive queries with emotional sensitivity
Banking & Finance	Rephrasing declined transaction responses apologetically; simplifying financial product explanations; communicating loan rejections with empathy
Education	Rephrasing student feedback constructively; simplifying academic instructions
Human Resources	Rephrasing interview feedback constructively; communicating policy updates empathetically
Legal	Rephrasing legal notices for clarity; simplifying contractual terms without losing accuracy
E-commerce	Rephrasing out-of-stock responses to maintain trust; communicating refund/return policies reassuringly
Social Media	Rephrasing user content for brand alignment; crafting empathetic responses to negative feedback
IT Support	Rephrasing troubleshooting steps for clarity; handling escalations professionally
Travel & Hospitality	Rephrasing cancellation responses to minimize dissatisfaction; simplifying travel updates

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Sample Output

Conversation:

Current intent: book a flight

User: I need to book a flight to New York, but I'm really anxious about traveling amidst the pandemic.
AI Agent: Can I know your preferred date of travel?
User: I'm planning to fly on the 3rd of next month.
AI Agent: Could you please tell me your city of departure?
User: I will be departing from Los Angeles.
AI Agent: What class of service do you prefer for your flight?
User: I prefer economy class, but I'm concerned. Are the flights safe?
AI Agent: Could you please let me know your preferred time of flight?

Rephrased response:

Absolutely, your worries are entirely understandable. However, please be assured that airlines are practicing stringent safety measures to ensure the well-being of all passengers. Now, could you please share your preferred flight time?

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Model Building Process

See Model Building Process.

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Model Benchmarks

Version	Accuracy	TPS	Latency (s)	Benchmark	Test Data
v1.0	99%	45	0.9	Summary v1	Results v1

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Version 1.0

Model Choice

Base model: Mistral 7B Instruct v0.2

Base Model	Developer	Language	Release Date	Status	Knowledge Cutoff
Mistral 7B Instruct v0.2	Mistral AI	Multi-lingual	March 2024	Static	September 2024

Fine-Tuning Parameters

Parameter	Description	Value
Load in 4-bit Precision	Reduce memory by loading weights at 4-bit	True
Use Double Quantization	Improve accuracy with double quantization	True
4-bit Quantization Type	Type of 4-bit quantization	nf4
Computation Data Type	Data type for 4-bit quantized weights	torch.float16
LoRA Rank	Rank of low-rank decomposition	32
LoRA Alpha	LoRA scaling factor	16
LoRA Dropout Rate	Dropout to prevent overfitting	0.05
Bias Term Inclusion	Add bias terms in LoRA layers	—
Task Type	LoRA task type	CAUSAL_LM
Targeted Modules	Layers where LoRA is applied	["query_key_value"]

General Parameters

Infrastructure: A10 (g5-xlarge).

Parameter	Description	Value
Learning Rate	Rate toward loss minimum	2e-4 (0.0002)
Batch Size	Examples per training step	2
Epochs	Passes over training data	4
Max Sequence Length	Maximum input length	32k
Optimizer	Optimization algorithm	paged_adamw_8bit

Benchmarks Summary v1

Comparison models: Mistral-vanilla, GPT-4.

See Test Data and Results v1 for full details.