Mitigating Object Hallucination in MLLMs via Data-augmented Phrase-level Alignment
Pritam Sarkar Sayna Ebrahimi Ali Etemad Ahmad Beirami Sercan O Arik Tomas Pfister
Despite their significant advancements, Multimodal Large Language Models
(MLLMs) often generate factually inaccurate information, referred to as hallucination.
In this work, we address object hallucinations in MLLMs, where information
is generated about an object not present in the input image. We introduce Data-augmented
Phrase-level Alignment (DPA), a novel loss which can be applied to
instruction-tuned off-the-shelf MLLMs to mitigate hallucinations, while preserving
their general vision-language capabilities. To fine-tune MLLMs with DPA, we first
generate a set of 'hallucinated' and 'correct' response pairs through generative data
augmentation by selectively altering the ground-truth information of the correct
responses at a phrase level. The DPA loss is then used to train MLLMs to reduce
the likelihood of hallucinated phrases compared to the correct ones. Our thorough
evaluation on various benchmarks confirms the effectiveness of DPA in mitigating
hallucination while retaining the out-of-the-box performance of the MLLMs on
general tasks. For instance, MLLMs finetuned with DPA, which we refer to as Hallucination
Attenuated Language and Vision Assistant (HALVA), improve F1 by up
to 13.4% on hallucination visual question-answering and reduce the hallucination
rate by up to 4.2% on image description tasks.
Multimodal LLMs, Object Hallucination, Vision-language Models
We introduce phrase-level alignment method that can be applied to off-the-shelf MLLMs for mitigating hallucinations, while preserving their general vision-language capabilities.