The strong capability of large language models (LLMs) has been applied to information extraction (IE) through either retrieval augmented prompting or instruction tuning (IT). However, the best way to incorporate information with LLMs for IE remains an open question. In this paper, we explore Retrieval Augmented Instruction Tuning (RA-IT) for IE, focusing on the task of open named entity recognition (NER). Specifically, for each training sample, we retrieve semantically similar examples from the training dataset as the context and prepend them to the input of the original instruction. To evaluate our RA-IT approach more thoroughly, we construct a Chinese IT dataset for open NER and evaluate RA-IT in both English and Chinese scenarios. Experimental results verify the effectiveness of RA-IT across various data sizes and in both English and Chinese scenarios. We also conduct thorough studies to explore the impacts of various retrieval strategies in the proposed RA-IT framework.

Recent large language models have become popular for achieving state-of-the-art performance in various natural language processing tasks, especially in zero-shot applications where fine-tuning is not required. However, these models underperform in text classification compared to fine-tuned models, due to limitations in reasoning ability and token constraints in in-context learning. Although extensive research explores large language models for text classification, few studies address multi-class classification with these models. This study introduces a new multi-class classification framework using a hierarchical label filtering strategy to manage long prompts in text classification. The influence of label sequence on classification accuracy is further investigated, demonstrating that optimized label arrangements significantly boost performance. Additionally, the study compares the performance of human-defined and model-generated labels in text classification, and analyze performance disparities across models for the same classification task.

Spiking Neural Networks (SNNs) are valued for their ability to process spatio-temporal information efficiently, offering biological plausibility, low energy consumption, and compatibility with neuromorphic hardware. However, the commonly used Leaky Integrate-and-Fire (LIF) model overlooks neuron heterogeneity and independently processes spatial and temporal information, limiting the expressive power of SNNs. In this paper, we propose the Dual Adaptive Leaky Integrate- and-Fire (DA-LIF) model, which introduces spatial and temporal tuning with independently learnable decays. Evaluations on both static (CIFAR10/100, ImageNet) and neuromorphic datasets (CIFAR10-DVS, DVS128 Gesture) demonstrate superior accuracy with fewer timesteps compared to state-of-the-art methods. Importantly, DA-LIF achieves these improvements with minimal additional parameters, maintaining low energy consumption. Extensive ablation studies further highlight the robustness and effectiveness of the DA-LIF model.

Nested NER tasks have some challenges in specific domains, such as biomedical and industrial fields, particularly due to low resource and class imbalance, which impede its wide application. In this study, we design a novel loss EIoU-EMC, by enhancing the implement of Intersection over Union loss and Multi-class loss. Our proposed method specially leverages the information of entity boundary and entity classification, thereby enhancing the model's capacity to learn from a limited number of data samples. To validate the performance of this innovative method in enhancing NER task, we conducted experiments on three distinct biomedical NER datasets and one dataset constructed by ourselves from industrial complex equipment maintenance documents. Comparing to strong baselines, our method demonstrates the competitive performance across all datasets. During the experimental analysis, our proposed method exhibits significant advancements in entity boundary recognition and entity classification. Our code and data are available at https://github.com/luminous11/EIoU-EMC/

Medical zero-shot relation triplet extraction, referred to as Med-ZeroRTE, requires the model to extract triplets comprising entities and relations from medical sentences. Importantly, the sentences include relations that were unseen during the model’s training phase. While Med-ZeroRTE had not been formally explored before this work, the limited availability of medical datasets, influenced by privacy concerns and annotation costs, emphasizes the necessity of exploring Med-ZeroRTE. This exploration faces two main challenges: Firstly, there is a gap of work specifically focused on triplet extraction from medical text in a zero-shot setting. Secondly, while a few approaches tackle the general zero-shot problems by employing generative models to produce synthetic data for unseen classes, the quality of some synthetic data remains suboptimal. Therefore, we propose a novel Enhanced Generator - Discriminator - Extractor framework (EGDE), which consists of three core modules, a prompt-tuned generator for generating synthetic samples given unseen relations, a fine-tuned discriminator for filtering qualified synthetic samples, a prompt-tuned extractor for extracting predicted medical triplets, to resolve Med-ZeroRTE and mitigate issues related to poor synthetic samples. The proposed framework is shown to be effective and superior compared to several robust baselines in experiments conducted on two distinct dataset settings.

Robotic Process Automation (RPA) has been widely used in business and enterprises to automate the processing of digital documents and collect information and acquire knowledge. Table structure reconstruction in scanned documents has been extensively studied as an essential application of RPA. However, the detection of table borders often ignores broken borders, which makes it unsuitable for natural scenes. To address this, our paper heavily employs a data augmentation approach to synthesize fake scanned documents to train our table-border semantic segmentation model. We propose a novel segmentation model for table borders based on semantic segmentation. We compare traditional morphology-based line detection algorithms with existing semantic segmentation-based approaches. The results indicate that our proposed algorithm can solve the frame line detection problem effectively, even for low-quality scanned images. Actual cases show that we can reconstruct the table’s structure and obtain the knowledge in the table.

Large language models (LLMs) exhibited powerful capability in various natural language processing tasks.This work focuses on exploring LLM performance on zero-shot information extraction, with a focus on the ChatGPT and named entity recognition (NER) task.Inspired by the remarkable reasoning capability of LLM on symbolic and arithmetic reasoning, we adapt the prevalent reasoning methods to NER and propose reasoning strategies tailored for NER.First, we explore a decomposed question-answering paradigm by breaking down the NER task into simpler subproblems by labels.Second, we propose syntactic augmentation to stimulate the model's intermediate thinking in two ways: syntactic prompting, which encourages the model to analyze the syntactic structure itself, and tool augmentation, which provides the model with the syntactic information generated by a parsing tool.Besides, we adapt self-consistency to NER by proposing a two-stage majority voting strategy, which first votes for the most consistent mentions, then the most consistent types.The proposed methods achieve remarkable improvements for zero-shot NER across seven benchmarks, including Chinese and English datasets, and on both domainspecific and general-domain scenarios.In addition, we present a comprehensive analysis of the error types with suggestions for optimization directions.We also verify the effectiveness of the proposed methods on the few-shot setting and other LLMs. 1 * Corresponding authors. 1 Code available at: https://github.com/Emma1066/ Zero-Shot-NER-with-ChatGPT Input Text: The player who temporarily ranks second is German athlete Bao Lizzo, with a total score of 355.02 points, slightly lower than Lanwei.Gold Label: {"German":"Geo-Political Entity", "Lanwei": "Person", "BaoꞏLizzo": "Person"} Vanilla Ans: {"German athlete Bao Lizzo": "Person", "Lanwei": "Person"} TS-SC Ans: {"BaoꞏLizzo": "Person": "Person", "Lanwei": "Person", "German": "Geo-Political Entity"} ----------------------------------

An image usually contains not only visual information but also higher-level semantic information. Nevertheless, previous computer vision algorithms, such as target detection and image classification, use only the visual features of the image alone. Recently, the explosion of scene graphs in computer vision has led to the challenge of generating structured scene graphs with rich semantic information. This paper proposes a one-stage query-based end-to-end Transformer model and generates scene graphs using the Hungarian matching algorithm. We develop an anti-bias reasoner module to reduce the impact of the unbalanced data distribution. Time-division training strategy is proposed to improve model training efficiency and speed up model convergence while improving model training performance. Experiments on the large-scale dataset Visual Genome were conducted in order to confirm the validity of our method. Compared with the existing state-of-the-art method, our method guarantees inference speed while maintaining acceptable performance and is more suitable for tasks with high real-time performance. Our work demonstrates that the one-stage method has great potential for exploration in scene graph generation.

As a biometric feature, handwritten signatures have various applications in finance, law, and business. The existing signature verification methods are mostly based on convolutional neural networks or Transformer based models. In this paper, we aim to implement writer independent offline handwritten signature verification by proposing an end-to-end method, Signature Verification Graph Convolutional Network (SigGCN). In SigGCN, signature images are first transformed into graphstructured data with additional position embedding to retain spatial information. The reason for this is that we expect graphstructured data to perform better in the capture of complicated relationships than CNN-based and Transformer based networks. We then use a multi-layer graph convolutional network to aggregate node information while updating the graph information. After obtaining the graph representation of signatures, efficient model training is performed using our proposed margin-based focal loss function to calculate the loss based on the Euclidean distance of two signatures. We conduct experiments on the CEDAR, BHSig260-Bengali, and BHSig260-Hindi datasets, and results obtained show that the proposed approach achieves remarkable performance and demonstrates great potential in solving real-world verification problems.

Computer supported cooperative work (CSCW) systems have been widely used to support teamwork in various fields such as design, education, research projects, etc. However, there is a great deal of knowledge generated directly or indirectly in the process of collaboration which is not well utilized and usually ignored instead of being reused and shared to help improve work efficiency. To bridge the gap between knowledge generation and utilization through CSCW, in this paper, we propose a knowledge mining based collaborative framework and first apply it to manufacturing value chains to achieve better work efficiency and reduce repetitive work in collaboration. Overall, this paper introduces the following novel insights and innovations: (1) we argue that there is a gap between the generated knowledge and the utilization of it during cooperative work; (2) a novel collaborative framework with knowledge mining approaches is proposed to bridge the gap; (3) a prototype system is further built and first applied to the manufacturing value chains. To the best of our knowledge, we are the first one to deal with knowledge reusing in CSCW of manufacturing value chains.

Physical knowledge is the foundation of most engineering fields in particular such as product design, analysis, and operation and maintenance. However, due to the complexity of physical concepts, laws, and calculations, students can be easily overwhelmed by the conceptual ideas in the process of learning physics. This paper proposes a new way for helping students grasp the logical relation between the physics knowledge points based on neural networks and knowledge graph technology. Specifically, we use Python scripts to collect the articles about physics knowledge on the Internet as the raw data. After removing the special characters and other irrelevant text, the rest of the data is passed to several neural networks based on BERT and ERNIE for their effective and efficient classification into seven kinds of physics knowledge. The experimental results show that using ERNIE-BERT for embedding and using RCNN for the downstream model achieve the best performance. Knowledge graph is used to build a tree structure of physics knowledge, holding the physics knowledge picked out by the neural networks under corresponding nodes.