A Novel Approach for De-Biasing and Enhanced Hallucination Mitigation in LLM

Hallucination in large language models refers to the typical mistakes we encounter when relying on AI systems, such as ChatGPT-like models, in our daily lives, a situation where the model produces inaccurate, illogical, or fake text. This happens because large language models (LLMs), which produce text based on patterns, are neither databases nor search engines and associations discovered in their training data instead of referencing particular references. Resolving hallucinations is crucial to promoting constructive human-AI interactions and increasing confidence in AI-generated material. Monitoring hallucinations is undoubtedly challenging, but it can be a game-changer as it enhances data verification at the source level and beyond.