RAGate: Enhancing Conversational AI with Adaptive Knowledge Retrieval

The rapid advancement of Large Language Models (LLMs) has greatly improved conversational systems, but they still face issues such as outdated knowledge and non-factual content. RAGate is proposed as an adaptive solution to enhance conversational AI by selectively augmenting responses with external knowledge based on context and human judgments. This approach aims to make responses more accurate, reliable, and contextually appropriate, utilizing variants like RAGate-Prompt, RAGate-PEFT, and RAGate-MHA. Extensive experiments demonstrate that RAGate improves response quality and reduces hallucinated outputs.