ReGraP-LLaVA: Reasoning enabled Graph-based Personalized Large Language and Vision Assistant (2505.03654v2)

Abstract: Recent advances in personalized MLLMs enable effective capture of user-specific concepts, supporting both recognition of personalized concepts and contextual captioning. However, humans typically explore and reason over relations among objects and individuals, transcending surface-level information to achieve more personalized and contextual understanding. To this end, existing methods may face three main limitations: Their training data lacks multi-object sets in which relations among objects are learnable. Building on the limited training data, their models overlook the relations between different personalized concepts and fail to reason over them. Their experiments mainly focus on a single personalized concept, where evaluations are limited to recognition and captioning tasks. To address the limitations, we present a new dataset named ReGraP, consisting of 120 sets of personalized knowledge. Each set includes images, KGs, and CoT QA pairs derived from the KGs, enabling more structured and sophisticated reasoning pathways. We propose ReGraP-LLaVA, an MLLM trained with the corresponding KGs and CoT QA pairs, where soft and hard graph prompting methods are designed to align KGs within the model's semantic space. We establish the ReGraP Benchmark, which contains diverse task types: multiple-choice, fill-in-the-blank, True/False, and descriptive questions in both open- and closed-ended settings. The proposed benchmark is designed to evaluate the relational reasoning and knowledge-connection capability of personalized MLLMs. We conduct experiments on the proposed ReGraP-LLaVA and other competitive MLLMs. Results show that the proposed model not only learns personalized knowledge but also performs relational reasoning in responses, achieving the SoTA performance compared with the competitive methods. All the codes and datasets are released at: https://github.com/xyfyyds/ReGraP.

An Analytical Overview of ReGraP-LLaVA: Multimodal Personalized Language and Vision Assistant

The paper presents a sophisticated approach to enhancing multimodal LLMs (MLLMs) with personalization and reasoning capabilities by introducing ReGraP-LLaVA—an innovative model that integrates graph-based reasoning with personalized multimodal assistance. This research brings to light the limitations of existing personalized MLLMs, highlights the importance of relational reasoning, and proposes a comprehensive dataset and benchmark for evaluating personalized MLLMs.

Key Contributions and Methodology

The authors identify three main challenges in current personalized MLLMs: the inadequacy of training data for learning multi-object relations, the neglect of relational knowledge in models, and the narrow focus of experiments on single personalized concepts. To overcome these challenges, the paper introduces three pivotal components: the ReGraP dataset, the ReGraP-LLaVA model, and the ReGraP benchmark.

1. ReGraP Dataset: This dataset is a distinct feature of the paper, offering 120 sets of personalized knowledge encompassing images, knowledge graphs (KGs), and Chain-of-Thought Question-Answering (CoT QA) pairs. The set structure facilitates learning sophisticated relational pathways beyond mere recognition and captioning.
2. ReGraP-LLaVA Model: Built upon existing LLaVA architectures, ReGraP-LLaVA incorporates newly designed soft and hard graph prompting methods. These enable alignment of KGs within the model’s semantic space, enhancing its capability to learn and reason over personalized knowledge. By leveraging CoT QA pairs alongside graph-based prompts, the model achieves state-of-the-art (SoTA) performance across tests compared to competitive MLLMs.
3. ReGraP Benchmark: The benchmark tasks are diverse, including multiple-choice, fill-in-the-blank, and descriptive questions in both open- and closed-ended formats. This heterogeneity serves to evaluate not only knowledge acquisition but also relational reasoning, a significant stride away from typical evaluation metrics bound to recognition and captioning.

Quantitative Results

The experimental results demonstrate that ReGraP-LLaVA outperforms baselines in relational reasoning capabilities while maintaining high-level personalized knowledge acquisition. The model excels particularly in complex tasks requiring relational understanding, showcasing significant performance improvement across various metrics.

Implications and Future Directions

The integration of graph-based reasoning in personalized MLLMs can have profound implications: it enhances the ability of AI to emulate human-like understanding of contextual and relational knowledge. The model proposed in the paper could find applications in areas needing deep personalized interactions, such as educational tools, digital assistants, and therapeutic applications.

Future research directions suggested by the paper include optimizing the computational efficiency of graph-prompting methods and potentially simplifying the model architecture to maintain its robust reasoning capability without incurring extensive computational costs. Additionally, exploring the model’s adaptability to streaming personal data could lead to real-time enhancements in personalization.

Conclusion

The ReGraP-LLaVA framework introduced by this paper represents a notable advancement in the personalized AI domain, offering a robust model capable of reasoning over personalized, contextually rich multimodal inputs. Its structured approach to integrating reasoning through graph-based methods opens up exciting possibilities for further research in leveraging graph structures for enhanced AI personalization and contextual understanding. The methodology and findings presented set a valuable precedent for future explorations into sophisticated personal AI interactions and capabilities.