ConfQA: Answer Only If You Are Confident
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%.
6847b8793ec10bdd8ab4df4f
Yin Huang
extracted_pending
2025-06-10T04:45:46.729Z
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Yin Huang
user
MaggieHuang
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Yifan Ethan Xu
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Kai Sun
6847b8793ec10bdd8ab4df52
Vera Yan
6847b8793ec10bdd8ab4df53
Alicia Sun
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Haidar Khan
6847b8793ec10bdd8ab4df55
Jimmy Nguyen
6847b8793ec10bdd8ab4df56
Mohammad Kachuee
6847b8793ec10bdd8ab4df57
Zhaojiang Lin
6847b8793ec10bdd8ab4df58
Yue Liu
6847b8793ec10bdd8ab4df59
Aaron Colak
6847b8793ec10bdd8ab4df5a
Anuj Kumar
6847b8793ec10bdd8ab4df5b
Wen-tau Yih
6847b8793ec10bdd8ab4df5c
Xin Luna Dong
ConfQA: Answer Only If You Are Confident
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%.
ConfQA fine-tuning strategy reduces factual statement hallucination in LLMs by 80%, using a dampening prompt and factual statements from knowledge graphs to improve confidence calibration and knowledge selection.
Large Language Models LLMs fine-tuning ConfQA hallucination factuality benchmarks dampening prompt factual statements knowledge graphs confidence calibration Dual Neural Knowledge framework neural knowledge symbolic knowledge accuracy gains external retrievals