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Reducing AI Hallucinations: A Crucial Priority As business leaders increasingly look to adopt AI, we need to make evaluating and mitigating risks like AI "hallucinations" a top priority. Hallucinations refer to factual inaccuracies or unverified speculative content generated by AI systems. Researchers recently surveyed 32 techniques to diminish hallucinations in large language models (LLMs), the backbone of many AI apps. Here are three leading approaches: 1. Retrieval-Augmented Generation (RAG): Enhances LLMs by retrieving and conditioning responses on authoritative external knowledge bases. Effective at improving accuracy but can be resource intensive to implement at scale. 2. Chain-of-Reasoning (CoR): LLMs verify their own responses through automatically generated questioning. Promising for boosting accuracy with additional computation cost. 3. Knowledge Retrieval: Leverages model output values to validate content against known facts. Easier to integrate but dependent on efficiency of retrieval systems. Testing required, but focusing on hallucination mitigation abilities alongside practical implementation considerations allows us to create safer, more responsible AI. Reference: A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models: https://lnkd.in/eDqzVjkk My evaluation criteria: - Effectiveness in Reducing Hallucinations: How well does the method mitigate inaccuracies? - Integration with Existing Systems: Can the method be easily integrated into your current technological infrastructure? - Scalability: Is the method suitable for your operation's scale? - Ease of Implementation: Consider the learning curve and resource requirements. - Cost: Evaluate both upfront and ongoing costs. - Performance Metrics: Look at speed, accuracy, and other relevant performance indicators. Results: Retrieval-Augmented Generation (RAG): A technique to enhance the responses of Large Language Models by incorporating information from external authoritative sources. - Effectiveness: High. It enhances response accuracy by leveraging external, authoritative knowledge bases. - Integration: Moderate. It may require adjustments to integrate with existing LLM infrastructures but utilizes pre-trained models for easier integration. - Scalability: High. Suitable for large-scale operations due to its robust external knowledge base. - Ease of Implementation: Moderate. Involves understanding and implementing seq2seq models and Dense Passage Retrieval. - Cost: Moderate to High. While using pre-trained models reduces initial costs, integrating and maintaining a dense vector index can be resource-intensive. - Performance Metrics: Good. Offers improved accuracy and relevance, although speed might be impacted due to external data retrieval.