LLM Hallucinations refer to instances where large language models (LLMs) like GPT-3 generate information that is incorrect, misleading, or not based on any factual data. These hallucinations can arise because the models are designed to generate plausible text based on patterns in the data they were trained on, rather than verifying the factual accuracy of every statement.
Why are LLM Hallucinations important?
LLM hallucinations are important because they can lead to the spread of misinformation, reduce the reliability of AI-generated content, and potentially cause harm if users act on incorrect information. In customer service, hallucinations can negatively impact customer trust and satisfaction if the AI provides incorrect or misleading responses.
How to improve the LLM Hallucinations rate?
- Training and Fine-Tuning: Use domain-specific datasets to train and fine-tune the LLM, ensuring it has access to accurate and relevant information.
- Human-in-the-Loop: Implement a system where human agents can review and correct the AI’s responses, especially in critical situations.
- Knowledge Bases: Integrate the LLM with up-to-date and verified knowledge bases or databases, ensuring the AI can reference accurate information.
- Post-Processing: Develop post-processing algorithms to verify the factual accuracy of the AI’s responses before they are delivered to the customer.
- Feedback Loops: Collect customer feedback on AI responses and use this data to continuously improve the model’s accuracy.
- Transparency and Alerts: Make the AI transparent about its capabilities and limitations, and alert users when it is unsure or when its responses may not be fully accurate.
By addressing LLM hallucinations, organizations can enhance the reliability and trustworthiness of AI in customer service applications.
Teneo can work with LLM (including RAG) and AI. However, it also allows you to exclude specific knowledge areas from LLM and AI and implement your own input processing for those instead. If for whatever reason the hallucination problem cannot be resolved on the LLM level or with RAG, you can effectively circumvent it by implementing our own input processing for the affected knowledge area.
More information
- https://www.teneo.ai/blog/avoid-llm-hallucinations
- https://www.teneo.ai/blog/teneos-advanced-ai-in-customer-service-operations-mastering-rag-to-minimize-llm-hallucinations
- https://www.teneo.ai/blog/how-to-avoid-llm-hallucinations-the-best-kept-secret
- https://www.teneo.ai/blog/5-ways-to-deal-with-llm-hallucinations
- https://www.teneo.ai/blog/navigating-llm-hallucinations-with-teneo
- https://www.teneo.ai/blog/how-to-get-controlled-outputs-in-llms-large-language-models