User-Controlled Knowledge Fusion in Large Language Models: Balancing Creativity and Hallucination

Understand

In modern dialogue systems, the use of Large Language Models (LLMs) has grown exponentially due to their capacity to generate diverse, relevant, and creative responses.

• Despite their strengths, striking a balance between the LLMs' creativity and their faithfulness to external knowledge remains a key challenge.
• This paper presents an innovative user-controllable mechanism that modulates the balance between an LLM's imaginative capabilities and its adherence to factual information.
• Our approach incorporates a numerical tag during the fine-tuning phase of the LLM's training, representing the degree of faithfulness to the reference knowledge in the generated responses.