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Open-Source ‘Parlant’ Fixes Hallucinations in Enterprise GenAI Chatbots
- Businesses are exploring the potential of generative AI in customer service, but face challenges due to 'hallucinations' in Large Language Models (LLMs).
- LLMs generate responses probabilistically, leading to unpredictable deviations in critical service protocols, posing risks in high-stakes environments.
- Traditional solutions like LangFlow and Rasa attempt to confine LLM responses but often require manual edits and still result in critical hallucinations.
- Enterprises have been hesitant to deploy customer-facing GenAI due to the risks posed by LLM hallucinations.
- An open-source framework called Parlant, used by major financial companies, offers a solution by enabling control over user-facing AI agents.
- Parlant's AI Conversation Modeling system allows precise control over GenAI communications by managing pre-approved 'utterances' dynamically.
- Parlant offers 'Fluid Composition' mode for creating and refining approved utterances, ensuring predictability while maintaining AI's natural capabilities.
- The system switches to 'strict' mode during runtime to construct responses only from pre-approved utterances based on the conversation context.
- Parlant is LLM-agnostic and supports multiple LLM providers, enhancing the control over AI agents.
- Emcie's research study on 'Attentive Reasoning Queries' (ARQs) offers methods to optimise instruction-following in LLMs, outperforming free-form reasoning approaches.
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