Cognitive diagnostics in the Web-based Intelligent Education System (WIES) aims to assess students' mastery of knowledge concepts from heterogeneous, noisy interactions. Recent work has tried to utilize Large Language Models (LLMs) for cognitive diagnosis, yet LLMs struggle with structured data and are prone to noise-induced misjudgments. Specially, WIES's open environment continuously attracts new students and produces vast amounts of response logs, exacerbating the data imbalance and noise issues inherent in traditional educational systems. To address these challenges, we propose DLLM, a Diffusion-based LLM framework for noise-robust cognitive diagnosis. DLLM first constructs independent subgraphs based on response correctness, then applies relation augmentation alignment module to mitigate data imbalance. The two subgraph representations are then fused and aligned with LLM-derived, semantically augmented representations. Importantly, before each alignment step, DLLM employs a two-stage denoising diffusion module to eliminate intrinsic noise while assisting structural representation alignment. Specifically, unconditional denoising diffusion first removes erroneous information, followed by conditional denoising diffusion based on graph signal to eliminate misleading information. Finally, the noise-robust representation that integrates semantic knowledge and structural information is fed into existing cognitive diagnosis models for prediction. Experimental results on three publicly available web-based educational platform datasets demonstrate that our DLLM achieves optimal predictive performance across varying noise levels, which demonstrates that DLLM achieves noise robustness while effectively leveraging semantic knowledge from LLM.

Extracting individual sentences from a document as evidence or reasoning steps is commonly done in many NLP tasks.However, extracted sentences often lack context necessary to make them understood, e.g., coreference and background information.To this end, we propose a content selection and planning framework for zero-shot decontextualisation, which determines what content should be mentioned and in what order for a sentence to be understood out of context.Specifically, given a potentially ambiguous sentence and its context, we first segment it into basic semanticallyindependent units.We then identify potentially ambiguous units from the given sentence, and extract relevant units from the context based on their discourse relations.Finally, we generate a content plan to rewrite the sentence by enriching each ambiguous unit with its relevant units.Experimental results demonstrate that our approach is competitive for sentence decontextualisation, producing sentences that exhibit better semantic integrity and discourse coherence, outperforming existing methods.

Current research for node classification focuses on dealing with either graph noise or label noise, but few studies consider both of them. In this paper, we propose a new robust node classification method to simultaneously deal with graph noise and label noise. To do this, we design a graph contrastive loss to conduct local graph learning and employ self-attention to conduct global graph learning. They enable us to improve the expressiveness of node representation by using comprehensive information among nodes. We also utilize pseudo graphs and pseudo labels to deal with graph noise and label noise, respectively. Furthermore, We numerically validate the superiority of our method in terms of robust node classification compared with all comparison methods.

Selecting which claims to check is a timeconsuming task for human fact-checkers, especially from documents consisting of multiple sentences and containing multiple claims.However, existing claim extraction approaches focus more on identifying and extracting claims from individual sentences, e.g., identifying whether a sentence contains a claim or the exact boundaries of the claim within a sentence.In this paper, we propose a method for documentlevel claim extraction for fact-checking, which aims to extract check-worthy claims from documents and decontextualise them so that they can be understood out of context.Specifically, we first recast claim extraction as extractive summarization in order to identify central sentences from documents, then rewrite them to include necessary context from the originating document through sentence decontextualisation.Evaluation with both automatic metrics and a fact-checking professional shows that our method is able to extract check-worthy claims from documents more accurately than previous work, while also improving evidence retrieval.

Anomaly detection is a crucial technique for comprehending the intricate structures of data, and it has garnered significant attention in various real-life applications, including finance, transportation, and network security. Presently, existing anomaly detection methods primarily concentrate on shallow techniques like residual analysis and community discovery, as well as deep learning methods that employ self-encoders as the underlying framework. However, these current methods overlook the local information during training, resulting in subpar and unexplained results. To recognize the local information, in this paper, we propose a novel Local Information Recognition method for Attribute Graph Anomaly Detection (LRAGAD). Specifically, to use the contextual structural information, LRAGAD first constructs a contrastive learning representation by generating different substructures from the target nodes, while reconstructing the whole graph using self-encoders that can utilize the neighborhood information of target nodes. Moreover, to better understand the complex graph structure, LRAGAD uses anomaly score estimation for outlier prediction. Experimental results on five real-world datasets demonstrate that the proposed LRAGAD method obtains better performance on AUC scores.

Graph Neural Networks (GNN) are an effective technique for node classification, but their performance is easily affected by the quality of the primitive graph and the limited receptive field of message-passing. In this paper, we propose a new self-attention method, namely Chain of Propagation Prompting (CPP), to address the above issues as well as reduce dependence on label information when employing self-attention for node classification. To do this, we apply the self-attention framework to reduce the impact of a low-quality graph and to obtain a maximal receptive field for the message-passing. We also design a simple pattern of message-passing as the prompt to make self-attention capture complex patterns and reduce the dependence on label information. Comprehensive experimental results on real graph datasets demonstrate that CPP outperforms all relevant comparison methods.

Multi-hop question answering (QA) requires reasoning over multiple documents to answer a complex question and provide interpretable supporting evidence. However, providing supporting evidence is not enough to demonstrate that a model has performed the desired reasoning to reach the correct answer. Most existing multi-hop QA methods fail to answer a large fraction of sub-questions, even if their parent questions are answered correctly. In this paper, we propose the Prompt-based Conservation Learning (PCL) framework for multi-hop QA, which acquires new knowledge from multi-hop QA tasks while conserving old knowledge learned on single-hop QA tasks, mitigating forgetting. Specifically, we first train a model on existing single-hop QA tasks, and then freeze this model and expand it by allocating additional sub-networks for the multi-hop QA task. Moreover, to condition pre-trained language models to stimulate the kind of reasoning required for specific multi-hop questions, we learn soft prompts for the novel sub-networks to perform type-specific reasoning. Experimental results on the HotpotQA benchmark show that PCL is competitive for multi-hop QA and retains good performance on the corresponding single-hop sub-questions, demonstrating the efficacy of PCL in mitigating knowledge loss by forgetting.

Effective multi-hop question answering (QA) requires reasoning over multiple scattered paragraphs and providing explanations for answers. Most existing approaches cannot provide an interpretable reasoning process to illustrate how these models arrive at an answer. In this paper, we propose a Question Decomposition method based on Abstract Meaning Representation (QDAMR) for multi-hop QA, which achieves interpretable reasoning by decomposing a multi-hop question into simpler subquestions and answering them in order. Since annotating the decomposition is expensive, we first delegate the complexity of understanding the multi-hop question to an AMR parser. We then achieve decomposition of a multi-hop question via segmentation of the corresponding AMR graph based on the required reasoning type. Finally, we generate sub-questions using an AMR-to-Text generation model and answer them with an off-the-shelf QA model. Experimental results on HotpotQA demonstrate that our approach is competitive for interpretable reasoning and that the sub-questions generated by QDAMR are well-formed, outperforming existing question-decomposition-based multihop QA approaches.

The key challenge of co-saliency detection is to extract discriminative features to distinguish the common salient foregrounds from backgrounds in a group of relevant images. In this paper, we propose a new co-saliency detection framework which includes two strategies to improve the discriminative ability of the features. Specifically, on one hand, we segment each image to semantic superpixel clusters as well as generate different scales/sizes of images for each input image by the VGG-16 model. Different scales capture different patterns of the images. As a result, multi-scale images can capture various patterns among all images by many kinds of perspectives. Second, we propose a new method of Graph Convolutional Network (GCN) to fine-tune the multi-scale features, aiming at capturing the common information among the features from all scales and the private or complementary information for the feature of each scale. Moreover, the proposed GCN method jointly conducts multi-scale feature fine-tune, graph learning, and feature learning in a unified framework. We evaluated our method on three benchmark data sets, compared to state-of-the-art co-saliency detection methods. Experimental results showed that our method outperformed all comparison methods in terms of different evaluation metrics.