Mitigating Object Hallucinations via Sentence-Level Early Intervention
Authors: name: Shangpin Peng, name: Senqiao Yang, name: Li Jiang, name: Zhuotao Tian

Multimodal large language models (MLLMs) have revolutionized cross-modal
understanding but continue to struggle with hallucinations - fabricated content
contradicting visual inputs. Existing hallucination mitigation methods either
incur prohibitive computational costs or introduce distribution mismatches
between training data and model outputs. We identify a critical insight:
hallucinations predominantly emerge at the early stages of text generation and
propagate through subsequent outputs. To address this, we propose **SENTINEL**
(**S**entence-level **E**arly i**N**tervention **T**hrough **IN**-domain
pr**E**ference **L**earning), a framework that eliminates dependency on human
annotations. Specifically, we first bootstrap high-quality in-domain preference
pairs by iteratively sampling model outputs, validating object existence
through cross-checking with two open-vocabulary detectors, and classifying
sentences into hallucinated/non-hallucinated categories. Subsequently, we use
context-coherent positive samples and hallucinated negative samples to build
context-aware preference data iteratively. Finally, we train models using a
context-aware preference loss (C-DPO) that emphasizes discriminative learning
at the sentence level where hallucinations initially manifest. Experimental
results show that SENTINEL can reduce hallucinations by over 90\% compared to
the original model and outperforms the previous state-of-the-art method on both
hallucination benchmarks and general capabilities benchmarks, demonstrating its
superiority and generalization ability. The models, datasets, and code are
available at https://github.com/pspdada/SENTINEL.