LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition

Grounded Multimodal Named Entity Recognition (GMNER) is a nascent multimodal task that aims to identify named entities, entity types and their corresponding visual regions. GMNER task exhibits two challenging properties: 1) The weak correlation between image-text pairs in social media results in a significant portion of named entities being ungroundable. 2) There exists a distinction between coarse-grained referring expressions commonly used in similar tasks (e.g., phrase localization, referring expression comprehension) and fine-grained named entities. In this paper, we propose RiVEG, a unified framework that reformulates GMNER into a joint MNER-VE-VG task by leveraging large language models (LLMs) as a connecting bridge. This reformulation brings two benefits: 1) It maintains the optimal MNER performance and eliminates the need for employing object detection methods to pre-extract regional features, thereby naturally addressing two major limitations of existing GMNER methods. 2) The introduction of entity expansion expression and Visual Entailment (VE) Module unifies Visual Grounding (VG) and Entity Grounding (EG). It enables RiVEG to effortlessly inherit the Visual Entailment and Visual Grounding capabilities of any current or prospective multimodal pretraining models. Extensive experiments demonstrate that RiVEG outperforms state-of-the-art methods on the existing GMNER dataset and achieves absolute leads of 10.65%, 6.21%, and 8.83% in all three subtasks.

Prompt ChatGPT In MNER: Improved multimodal named entity recognition method based on auxiliary refining knowledge from ChatGPT

Multimodal Named Entity Recognition (MNER) on social media aims to enhance textual entity prediction by incorporating image-based clues. Existing research in this domain has primarily focused on maximizing the utilization of potentially relevant information in images or incorporating external knowledge from explicit knowledge bases (KBs). However, these methods either neglect the necessity of providing the model with relevant external knowledge, or the retrieved external knowledge suffers from high redundancy. To address these problems, we propose a conceptually simple two-stage framework called Prompt ChatGPT In MNER (PGIM) in this paper. We leverage ChatGPT as an implicit knowledge engine to acquire auxiliary refined knowledge, thereby bolstering the model's performance in MNER tasks. Specifically, we first utilize a Multimodal Similar Example Awareness module to select suitable examples from a small number of manually annotated samples. These examples are then integrated into a formatted prompt template tailored to the MNER task, guiding ChatGPT to generate auxiliary refined knowledge. Finally, the acquired knowledge is integrated with the raw text and inputted into the downstream model for further processing. Extensive experiments show that our PGIM significantly outperforms all existing state-of-the-art methods on two classic MNER datasets.