> ## Documentation Index > Fetch the complete documentation index at: https://koreai.mintlify.app/llms.txt > Use this file to discover all available pages before exploring further. # XO GPT User Query Paraphrasing Model [Back to XO GPT Model Specifications](/ai-for-service/generative-ai-tools/xo-gpt-module#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 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. | *** ## 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 XO GPT Benefits ### Contextual Communication Adapts user queries to the conversation context, enabling accurate intent interpretation for meaningful interactions. See [Model Benchmarks](#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](/ai-for-service/generative-ai-tools/xogpt-model-specifications#model-building-process). *** ## Model Benchmarks | Version | Accuracy | TPS | Latency (s) | Benchmark | Test Data | | ------- | -------- | --- | ----------- | ------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | v1.0 | 97% | 43 | 0.54 | [Summary v1](#benchmarks-summary-v1) | [Results v1](https://github.com/Koredotcom/docs-v2/raw/refs/heads/main/ai-for-service/generative-ai-tools/test-date-and-results/xogpt-user-query-paraphrasing-v1.0.xlsx) | *** ## Version 1.0 ### Model Choice Base model: [Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/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. Benchmarks Summary v1 See [Test Data and Results v1](https://github.com/Koredotcom/docs-v2/raw/refs/heads/main/ai-for-service/generative-ai-tools/test-date-and-results/xogpt-user-query-paraphrasing-v1.0.xlsx) for full details.