ConfQA: Answer Only If You Are Confident
Authors: name: Yin Huang, name: Yifan Ethan Xu, name: Kai Sun, name: Vera Yan, name: Alicia Sun, name: Haidar Khan, name: Jimmy Nguyen, name: Mohammad Kachuee, name: Zhaojiang Lin, name: Yue Liu, name: Aaron Colak, name: Anuj Kumar, name: Wen-tau Yih, name: Xin Luna Dong

Can we teach Large Language Models (LLMs) to refrain from hallucinating
factual statements? In this paper we present a fine-tuning strategy that we
call ConfQA, which can reduce hallucination rate from 20-40% to under 5% across
multiple factuality benchmarks. The core idea is simple: when the LLM answers a
question correctly, it is trained to continue with the answer; otherwise, it is
trained to admit "I am unsure". But there are two key factors that make the
training highly effective. First, we introduce a dampening prompt "answer only
if you are confident" to explicitly guide the behavior, without which
hallucination remains high as 15%-25%. Second, we leverage simple factual
statements, specifically attribute values from knowledge graphs, to help LLMs
calibrate the confidence, resulting in robust generalization across domains and
question types. Building on this insight, we propose the Dual Neural Knowledge
framework, which seamlessly select between internally parameterized neural
knowledge and externally recorded symbolic knowledge based on ConfQA's
confidence. The framework enables potential accuracy gains to beyond 95%, while
reducing unnecessary external retrievals by over 30%.