A Knowledge Graph is a structured representation of knowledge that captures entities, their attributes, and relationships between entities. It is often used to model real-world information and support complex queries.

Posts

InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration

/in /by

Though Large Language Models (LLMs) have shown remarkable open-generation capabilities across diverse domains, they struggle with knowledge-intensive tasks. To alleviate this issue, knowledge integration methods have been proposed to enhance LLMs with domain-specific knowledge graphs using external modules. However, they suffer from data inefficiency as they require both known and unknown knowledge for fine-tuning. Thus, we study a novel problem of integrating unknown knowledge into LLMs efficiently without unnecessary overlap of known knowledge. Injecting new knowledge poses the risk of forgetting previously acquired knowledge. To tackle this, we propose a novel Infuser-Guided Knowledge Integration (InfuserKI) framework that utilizes transformer internal states to determine whether to enhance the original LLM output with additional information, thereby effectively mitigating knowledge forgetting. Evaluations on the UMLS-2.5k and MetaQA domain knowledge graphs demonstrate that InfuserKI can effectively acquire new knowledge and outperform state-of-the-art baselines by 9% and 6%, respectively, in reducing knowledge forgetting.

Read more

LLMs and MI Bring Innovation to Material Development Platforms

/in /by

In this paper, we introduce efforts to apply large language models (LLMs) to the field of material development. NEC is advancing the development of a material development platform. By applying core technologies corresponding to two material development steps, namely investigation activities (Read paper/patent) and experimental planning (Design Experiment Plan), the platform organizes documents such as papers and reports as well as data such as experimental results and then presents in an interactive way to users. In addition, with techniquesthat reflect physical and chemical principles into machine learning models, AI can learn even with limited data and accurately predict material properties. Through this platform, we aim to achieve the seamless integration of materials informatics (MI) with a vast body of industry literature and knowledge, thereby bringing innovation to the material development process.

Read more

KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models

/in /by

Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. To augment KGs with new knowledge, researchers proposed models for KG Completion (KGC) tasks such as link prediction, i.e., answering (h, p, ?) or (?, p, t) queries. Such models are usually evaluated with averaged metrics on a held-out test set. While useful for tracking progress, averaged single-score metrics cannotreveal what exactly a model has learned — or failed to learn. To address this issue, we propose KGxBoard: an interactive framework for performing fine-grained evaluation on meaningful subsets of the data, each of which tests individual and interpretable capabilities of a KGC model. In our experiments, we highlight the findings that we discovered with the use of KGxBoard, which would have been impossible to detect with standard averaged single-score metrics.

Read more