In this paper, we aim to improve the reasoning ability of large language models(LLMs) over knowledge graphs(KGs) to answer complex questions. Inspired by existing methods that design the interaction strategy between LLMs and KG, we propose an autonomous LLM-based agent framework, called KG-Agent, which enables a small LLM to actively make decisions until finishing the reasoning process over KGs. In KG-Agent, we integrate the LLM, multifunctional toolbox, KG-based executor, and knowledge memory, and develop an iteration mechanism that autonomously selects the tool and then updates the memory for reasoning over KG. To guarantee the effectiveness, we leverage program language to formulate the multi-hop reasoning process over the KG and synthesize a code-based instruction dataset to fine-tune the base LLM. Extensive experiments demonstrate that only using 10K samples for tuning LLaMA2-7B can outperform competitive methods using larger LLMs or more data, on both in-domain and out-domain datasets. Our code and data will be publicly released.

Multimodal Named Entity Recognition and Grounding (MNERG) aims to extract paired textual and visual entities from texts and images. It has been well explored through a two-step paradigm: initially identifying potential visual entities using object detection methods and then aligning the extracted textual entities with their corresponding visual entities. However, when it comes to fine-grained MNERG, the long-tailed distribution of textual entity categories and the performance of object detectors limit the effectiveness of traditional methods. Specifically, more detailed classification leads to many low-frequency categories, and existing object detection methods often fail to pinpoint subtle regions within images. To address these challenges, we propose the Granular Entity Mapper (GEM) framework. Firstly, we design a multi-granularity entity recognition module, followed by a reranking module based on the Multimodal Large Language Model (MLLM) to incorporate hierarchical information of entity categories, visual cues, and external textual resources collectively for accurate fine-grained textual entity recognition. Then, we utilize a pre-trained Large Visual Language Model (LVLM) as an implicit visual entity grounder that directly deduces relevant visual entity regions from the entire image without the need for bounding box training. Experimental results on the GMNER and FMNERG datasets demonstrate that our GEM framework achieves state-of-the-art results on the fine-grained content extraction task.