Precise Information Control in Long-Form Text Generation
Journal:
arXiv
Published Date:
Jun 6, 2025
Abstract
A central challenge in modern language models (LMs) is intrinsic
hallucination: the generation of information that is plausible but
unsubstantiated relative to 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, known as verifiable claims, without adding any
unsupported ones. For comprehensiveness, 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 intrinsically hallucinate in over 70% of outputs. To
alleviate this lack of faithfulness, we introduce a post-training framework,
using 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 verification task, underscoring the
potential of precisely grounded generation.
