Post
957
Exciting breakthrough in AI Hallucination Detection & Mitigation! THaMES (Tool for Hallucination Mitigations and EvaluationS), a groundbreaking end-to-end framework tackling one of AI's biggest challenges: hallucination in Large Language Models.
Key Technical Features:
• Automated QA Testset Generation using weighted sampling and batch processing
- Implements VectorStoreIndex for knowledge base construction
- Uses text-embedding-large-3 for semantic similarity
- Generates 6 question types: simple, reasoning, multi-context, situational, distracting, and double
• Advanced Hallucination Detection
- Utilizes fine-tuned NLI (deberta-v3-base-tasksource-nli)
- Implements HHEM-2.1-Open for factual consistency scoring
- Combines entailment and factual consistency for ensemble scoring
• Multiple Mitigation Strategies
- In-Context Learning with Chain-of-Verification (CoVe)
- Retrieval-Augmented Generation (RAG)
- Parameter-Efficient Fine-Tuning (PEFT) using LoRA
Real-world Results:
- GPT-4o showed significant improvement with RAG
- Llama-3.1 performed better with In-Context Learning
- PEFT significantly improved Llama-3.1's hallucination metrics
Why it matters:
This framework sets a new standard for reliable AI development by providing comprehensive tools to evaluate and mitigate hallucinations in LLMs. Perfect for AI researchers, developers, and organizations focused on building trustworthy AI systems
Key Technical Features:
• Automated QA Testset Generation using weighted sampling and batch processing
- Implements VectorStoreIndex for knowledge base construction
- Uses text-embedding-large-3 for semantic similarity
- Generates 6 question types: simple, reasoning, multi-context, situational, distracting, and double
• Advanced Hallucination Detection
- Utilizes fine-tuned NLI (deberta-v3-base-tasksource-nli)
- Implements HHEM-2.1-Open for factual consistency scoring
- Combines entailment and factual consistency for ensemble scoring
• Multiple Mitigation Strategies
- In-Context Learning with Chain-of-Verification (CoVe)
- Retrieval-Augmented Generation (RAG)
- Parameter-Efficient Fine-Tuning (PEFT) using LoRA
Real-world Results:
- GPT-4o showed significant improvement with RAG
- Llama-3.1 performed better with In-Context Learning
- PEFT significantly improved Llama-3.1's hallucination metrics
Why it matters:
This framework sets a new standard for reliable AI development by providing comprehensive tools to evaluate and mitigate hallucinations in LLMs. Perfect for AI researchers, developers, and organizations focused on building trustworthy AI systems