Cost-efficient Knowledge-based Question Answering with Large Language Models

Understand

Knowledge-based question answering (KBQA) is widely used in many scenarios that necessitate domain knowledge.

• Large language models (LLMs) bring opportunities to KBQA, while their costs are significantly higher and absence of domain-specific knowledge during pre-training.
• We are motivated to combine LLMs and prior small models on knowledge graphs (KGMs) for both inferential accuracy and cost saving.
• However, it remains challenging since accuracy and cost are not readily combined in the optimization as two distinct metrics.