Factuality on Demand: Controlling the Factuality-Informativeness Trade-off in Text Generation
Ziwei Gong, Yanda Chen, Julia Hirschberg, Chen Zhao, He He, Zhou Yu, Kathleen Mckeown

TL;DR
This paper introduces Factuality-Controlled Generation (FCG), a framework allowing users to specify factuality constraints in text generation, balancing factual accuracy and informativeness in responses.
Contribution
The paper proposes a novel FCG framework, synthetic training data for models, and evaluation methods for factuality and informativeness trade-offs.
Findings
Synthetic training improves factuality adherence
Models better balance factuality and informativeness
Framework adaptable to different application needs
Abstract
Large language models (LLMs) encode knowledge with varying degrees of confidence. When responding to queries, models face an inherent trade-off: they can generate responses that are less informative but highly factual, or more informative but potentially less accurate. Different applications demand different balances between informativeness and factuality. We introduce Factuality-Controlled Generation (FCG), a framework that enables users to specify factuality constraints alongside their queries. We propose to evaluate FCG performance on two dimensions: adherence to factuality constraints and response informativeness. We propose to train models on the FCG task using synthetic data, and show that our synthetic training significantly improves models' ability to both respect factuality requirements and maintain informativeness in their outputs.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · Multimodal Machine Learning Applications · Computational and Text Analysis Methods
