XO GPT User Query Paraphrasing Model

Back to XO GPT Model Specifications The XO GPT User Query Paraphrasing model improves NLP accuracy by expanding and rephrasing user queries using conversation context. It resolves co-references and ambiguities before the query reaches downstream NLP components, enabling more accurate intent detection and entity recognition.

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 queries to external models raises privacy and security concerns.
Lack of Customization	General-purpose models aren’t 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.

Key Assumptions

Designed for text-based conversations only.
Paraphrases the user query only when it references or co-refers to details from prior conversation context. Other queries are passed through unchanged.

Benefits

Contextual Communication

Adapts user queries to the conversation context, enabling accurate intent interpretation for meaningful interactions. See Model Benchmarks for performance insights.

Cost-Effective

For Enterprise Tier customers, XO GPT eliminates commercial model usage costs. Example comparison (100 input tokens/conversation, 10,000 daily interactions, 15 tokens/response):

Model	Input $/MTok	Output $/MTok	Input $/Year	Output $/Year	Total $/Year
GPT-4 Turbo	$30	$60	$10,950	$3,285	$14,235
GPT-4	$10	$30	$3,650	$1,643	$5,293
GPT-4o Mini	$0.15	$0.60	$54.75	$32.85	$87.60

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.

Use Cases

Domain	Use Cases
Customer Support	Simplify complex queries for accurate intent detection; remove ambiguous references; enable contextual continuity across turns
Healthcare	Simplify complex patient inquiries; eliminate co-references for precise understanding of patient history or treatments
Banking & Finance	Clarify account action queries; simplify follow-up queries about previous interactions; clarify ambiguous product inquiries
Education	Clarify multi-part or context-heavy student queries; simplify questions about schedules or course content
Human Resources	Clarify ambiguous HR questions about benefits or leave policies; rephrase workplace policy questions
Legal	Simplify user queries about legal contracts or policies; clarify complex legal questions
E-commerce	Rephrase follow-up queries about orders or shipments; eliminate ambiguities in return/refund queries
Social Media	Clarify questions about flagged content or platform policies; simplify account settings or privacy queries
IT Support	Rephrase vague or context-dependent technical queries; eliminate co-references in user-reported issues
Travel & Hospitality	Clarify multi-part or ambiguous booking inquiries; simplify questions about changes to travel plans

Sample Output

Conversation:

User: Hi, can you help me select a University for studying Physics?
AI Agent: Sure, here are some top universities for Physics:
         1. Harvard University  2. MIT  3. Stanford  4. University of Cambridge.
         Which sounds best to you?
User: Which one is best in fee structure?
AI Agent: Generally, the most affordable undergraduate tuition for Physics is at Stanford.
User: Ok, I'll choose that one.

Paraphrased query:

User: Ok, I will choose to apply at Stanford University for a Physics course.

Model Building Process

See Model Building Process.

Model Benchmarks

Version	Accuracy	TPS	Latency (s)	Benchmark	Test Data
v1.0	97%	43	0.54	Summary v1	Results v1

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: Flan-T5, GPT-4.

See Test Data and Results v1 for full details.

​Challenges with Commercial Models

​Key Assumptions

​Benefits

​Contextual Communication

​Cost-Effective

​Enhanced Security

​Use Cases

​Sample Output

​Model Building Process

​Model Benchmarks

​Version 1.0

​Model Choice

​Fine-Tuning Parameters

​General Parameters

​Benchmarks Summary v1

Challenges with Commercial Models

Key Assumptions

Benefits

Contextual Communication

Cost-Effective

Enhanced Security

Use Cases

Sample Output

Model Building Process

Model Benchmarks

Version 1.0

Model Choice

Fine-Tuning Parameters

General Parameters

Benchmarks Summary v1