Reduce AI Hallucination Errors
Budget: ₹1,500 – ₹12,500 INR
I’m building an NLP-driven, multimodal assistant that accepts text, image, and audio inputs, but its replies still drift into hallucination. The goal is straightforward: sharpen response accuracy so the system stays firmly grounded in fact.
Right now the core pipeline is a Hugging Face Transformer model wrapped in a Retrieval-Augmented Generation (RAG) layer. I need you to audit the entire flow, diagnose where and why hallucinations appear, and then apply proven mitigation techniques. That could involve prompt engineering, better retrieval logic, truth-focused data augmentation, fine-tuning, or introducing guard-rail frameworks—whatever combination delivers measurably higher factual precision.
Deliverables
• A revised model or inference pipeline that demonstrably improves response accuracy (verified on my held-out benchmark).
• Evaluation report with automatic metrics (e.g., factual consistency, BERTScore) plus a small human review sample.
• Clean, reproducible code/notebooks configured for my AWS GPU instance.
Acceptance criteria
• ≥90 % accuracy on my 500-item test set with zero critical factual errors.
• Latency increase kept under 10 %.
• Documentation clear enough for a one-command retrain.
If you’re comfortable working with tools like PyTorch, LangChain, vector databases, and multimodal embeddings, let’s talk about your approach and timeline.
Right now the core pipeline is a Hugging Face Transformer model wrapped in a Retrieval-Augmented Generation (RAG) layer. I need you to audit the entire flow, diagnose where and why hallucinations appear, and then apply proven mitigation techniques. That could involve prompt engineering, better retrieval logic, truth-focused data augmentation, fine-tuning, or introducing guard-rail frameworks—whatever combination delivers measurably higher factual precision.
Deliverables
• A revised model or inference pipeline that demonstrably improves response accuracy (verified on my held-out benchmark).
• Evaluation report with automatic metrics (e.g., factual consistency, BERTScore) plus a small human review sample.
• Clean, reproducible code/notebooks configured for my AWS GPU instance.
Acceptance criteria
• ≥90 % accuracy on my 500-item test set with zero critical factual errors.
• Latency increase kept under 10 %.
• Documentation clear enough for a one-command retrain.
If you’re comfortable working with tools like PyTorch, LangChain, vector databases, and multimodal embeddings, let’s talk about your approach and timeline.