Precise Information Control in Long-Form Text Generation

A central challenge in language models (LMs) is faithfulness hallucination: the generation of information unsubstantiated by input context. To study this problem, we propose Precise Information Control (PIC), a new task formulation that requires models to generate long-form outputs grounded in a provided set of short self-contained statements, without adding any unsupported ones. PIC includes a full setting that tests a model’s ability to include exactly all input claims, and a partial setting that requires the model to selectively incorporate only relevant claims. We present PIC-Bench, a benchmark of eight long-form generation tasks (e. g. , summarization, biography generation) adapted to the PIC setting, where LMs are supplied with well-formed, verifiable input claims. Our evaluation of a range of open and proprietary LMs on PIC-Bench reveals that, surprisingly, state-of-the-art LMs still hallucinate against user-provided input in over 70% of generations. To alleviate this lack of faithfulness, we introduce a post-training framework that uses a weakly supervised preference data construction method to train an 8B PIC-LM with stronger PIC ability—improving from 69. 1% to 91. 0% F1 in the full PIC setting. When integrated into end-to-end factual generation pipelines, PIC-LM improves exact match recall by 17. 1% on ambiguous QA with retrieval, and factual precision by 30. 5% on a birthplace fact-checking task, underscoring the potential of precisely grounded generation.

Authors

• Jacqueline He
• Howard Yen
• Margaret Li
• Stella Li
• Zhiyuan Zeng
• Weijia Shi
• Yulia Tsvetkov
• Danqi Chen
• Pang Wei Koh
• Luke Zettlemoyer Meta AI

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