Web Science and Digital Libraries Research Group

As Natural Language Processing (NLP) evolves, Large Language Models (LLMs) are capable of interpreting text and extracting meaningful information. However, during my recent experimentation with Named Entity Recognition (NER) tasks, I found that different LLMs often produce varying outputs, much like humans offering diverse answers based on their unique perspectives. This leads to an interesting challenge: how can we unify the inconsistent entity list generated by multiple LLMs into a consistent and reliable output?  If you want to replicate these experiments, you can access the GitHub repository . Figure 1. Differing Entity Extraction Results from Two LLMs for the Same Text Input. The Challenge LLMs Capture Entities Differently For this experiment, I used Ollama , an open-source project that serves as a powerful and user-friendly platform for running LLMs on a local machine. Specifically, I employed Llama 3.1 8B (Meta), Phi 3.5 8B (Microsoft), and Gemma 2 9B (Google). During ...

          Figure 1. An example from DeepPatent2 extraction results for US Design Patent #0836880 (Figure 1 in DeepPatent2 ) Introduction Technical illustrations and drawings, unlike natural images, are designed for straightforward human comprehension, focusing on details such as strokes, lines, and shading rather than colors and gradients ( Carney and Levin, 2002 ) [2]. While computer vision has extensively studied natural images for object recognition and understanding context, technical drawings, prevalent in design patents, remain less explored. These drawings do not contain rich information such as color and gradient as appearing in natural images, making them distinct from typical natural images ( DeepPatent ) [6]. Recent developments in sketch-based datasets like QuickDraw [3], TU-Berlin [4], and ImageNet-Sketch [5] have provided valuable resources for sketch-based image retrieval. In this blog, we summarize our paper titled, " DeepPatent...