A Topic-level Self-Correctional Approach to Mitigate Hallucinations in MLLMs

Aligning the behaviors of Multimodal Large Language Models (MLLMs) with human preferences is crucial for developing robust and trustworthy AI systems. While recent attempts have employed human experts or powerful auxiliary AI systems to provide more accurate preference feedback, such as determining the preferable responses from MLLMs or directly rewriting hallucination-free responses, extensive resource overhead compromise the scalability of the feedback collection. In this work, we introduce Topic-level Preference Overwriting (TPO), a self-correctional approach that guide the model itself to mitigate its own hallucination at the topic level. Through a deconfounded strategy that replaces each topic within the response with the best or worst alternatives generated by the model itself, TPO creates more contrasting pairwise preference feedback, enhancing the feedback quality without human or proprietary model intervention. Notably, the experimental results demonstrate proposed TPO achieves state-of-the-art performance in trustworthiness, significantly reducing the object hallucinations by 92% and overall hallucinations by 38%. Code, model and data will be released.

RH20T-P: A Primitive-Level Robotic Dataset Towards Composable Generalization Agents

The ultimate goals of robotic learning is to acquire a comprehensive and generalizable robotic system capable of performing both seen skills within the training distribution and unseen skills in novel environments. Recent progress in utilizing language models as high-level planners has demonstrated that the complexity of tasks can be reduced through decomposing them into primitive-level plans, making it possible to generalize on novel robotic tasks in a composable manner. Despite the promising future, the community is not yet adequately prepared for composable generalization agents, particularly due to the lack of primitive-level real-world robotic datasets. In this paper, we propose a primitive-level robotic dataset, namely RH20T-P, which contains about 33000 video clips covering 44 diverse and complicated robotic tasks. Each clip is manually annotated according to a set of meticulously designed primitive skills, facilitating the future development of composable generalization agents. To validate the effectiveness of RH20T-P, we also construct a potential and scalable agent based on RH20T-P, called RA-P. Equipped with two planners specialized in task decomposition and motion planning, RA-P can adapt to novel physical skills through composable generalization. Our website and videos can be found at https://sites.google.com/view/rh20t-primitive/main. Dataset and code will be made available soon.