With the development of social media, people are exposed to a vast amount of unverified information, making fact-checking particularly important. Existing fact-checking methods primarily encourage breaking down claims into more easily solvable sub-tasks, and deriving final answers through reasoning with external evidence. However, these models face logical issues regarding whether and how the sub-tasks can logically be combined to form the original claims, and encounter causal errors in the reasoning process due to insufficient evidence or hallucinations from LLMs. In addition, they often suffer from a lack of interpretability. In this paper, we propose Logical and Causal fact-checking (LoCal), a novel fact-checking framework based on multiple LLM-based agents. The usage of multi-agent systems is due to their increasingly demonstrated ability to perform complex tasks in a manner similar to humans. LoCal primarily consists of a decomposing agent, multiple reasoning agents, and two evaluating agents. Specifically, the decomposing agent first utilizes the in-context learning ability of LLMs to break down complex claims into simpler sub-tasks, including fact verification tasks and question answering tasks. Afterwards, two types of reasoning agents are respectively utilized to retrieve external knowledge to address the fact verification tasks that require comparative analysis skills, and the question answering tasks that necessitate the ability of information extraction from evidence. We then combine the sub-tasks and their corresponding responses to generate a solution for evaluation. In order to enhance logical and causal consistency, two evaluating agents are respectively employed to examine whether the generated solution is logically equivalent to the original claim and determine whether the solution still holds when challenged by the counterfactual label. The evaluating agents provide confidence degrees for the solutions based on the evaluation results and iteratively correct the logical and causal errors in the reasoning process. We evaluate LoCal on two challenging datasets, and the results show that LoCal significantly outperforms all the baseline models across different settings of evidence availability. In addition, LoCal offers better interpretability by providing a structured solution along with detailed evaluating processes. We believe LoCal will provide valuable insights for future misinformation detection.

With the prevalence of online news services, personalized news recommendation (PNR) has played an indispensable role in meeting users' needs and mitigating information overload, with the aim of providing news articles that cater to user preferences. Despite significant progress made in the field of PNR over the past few decades, their performances are still hindered by some limitations, such as insufficient news modeling, difficulties in effectively modeling diverse user interests, and ignorance of fine-grained matching signals. It is fortunate that the emergence of large language models (LLMs) provides a promising insight into empowering the capabilities of news recommendation. Known for their impressive capabilities of natural language understanding and generation, LLMs have achieved disruptive achievements in various natural language processing (NLP) tasks, which motivates us to integrate LLMs into news recommendation and benefits from them to make up existing deficiencies. In this paper, we conduct a comprehensive review of current efforts made towards utilizing LLMs for PNR, with a focus on three core modules involved in the news recommendation process, i.e., news modeling, user modeling, and accurate matching. We systematically discuss and analyze relevant works under each focus. In addition, we point out several potential research directions to provide more inspiration for future investigation in this thriving field.

Incorporating explicit personas into dialogue models is critical for generating responses that fulfill specific user needs and preferences, creating a more personalized and engaging interaction. Early works on persona-based dialogue generation directly concatenate the persona descriptions and dialogue history into relatively small pre-trained language models (PLMs) for response generation, which leads to uninformative and inferior results due to the sparse persona information and the limited model generation capabilities. Recently, large language models (LLMs) have shown their surprising capabilities in language generation. Prompting the LLMs with the persona descriptions for role-playing dialogue generation has also achieved promising results. However, deploying LLMs is challenging for practical applications due to their large scale, spurring efforts to distill the generation capabilities into more concise and compact models through teacher-student learning. In this article, we propose an efficient compact K nowledge-grounded P ersona-based D ialogue model enhanced by LLM D istillation (KPDD). Specifically, first, we propose to enrich the annotated persona descriptions by integrating external knowledge graphs (KGs) with a mixed encoding network, coupled with a mixture of experts (MoE) module for both informative and diverse response generation. The mixed encoding network contains multiple layers of modality interaction operations, enabling information from both modalities propagates to the other. Second, to fully exploit the generation capabilities of LLMs, we turn to the distillation technique to improve the generation capabilities of our model, facilitated by a natural language inference (NLI)-based filtering mechanism to extract high-quality information from LLMs. In addition, we employ a curriculum learning strategy to train our model on the high-quality filtered distilled data and progressively on the relatively noisy original data, enhancing its adaptability and performance. Extensive experiments show that KPDD outperforms state-of-the-art baselines in terms of both automatic and human evaluation.

Visual Question Answering based on external Knowledge Bases (KB-VQA) requires a model to incorporate knowledge beyond the content of given image and question for answer prediction. Most existing works made efforts on using graph neural networks or Multi-modal Large Language Models to incorporate external knowledge for answer generation. Despite the promising results, they have limited interpretability and exhibit a deficiency in handling questions with unseen answers. In this paper, we propose a novel interpretable graph routing network (GRN) which explicitly conducts entity routing over a constructed scene knowledge graph step by step for KB-VQA. At each step, GRN keeps an entity score vector representing how likely of each entity to be activated as the answer, and a transition matrix representing the transition probability from one entity to another. To answer the given question, GRN will focus on certain keywords of the question at each step and correspondingly conduct entity routing by transiting the entity scores according to the transition matrix computed referring to the focused question keywords. In this way, it clearly provides the reasoning process of KB-VQA and can handle the questions with unseen answers without distinction. Experiments on the benchmark dataset KRVQA have demonstrated that GRN improves the performance of KB-VQA by a large margin, surpassing existing state-of-the art KB-VQA methods and Multi-modal Large Language Models, as well as shows competent capability in handling unseen answers and good interpretability in KB-VQA.

The Dialogue-level Aspect-based Sentiment Quadruple analysis (DiaASQ) task has recently received attention in the Aspect-Based Sentiment Analysis (ABSA) field. It aims to extract(target, aspect, opinion, sentiment) quadruples from multi-turn and multi-party dialogues. Compared to previous ABSA tasks focusing on text such as sentences, the DiaASQ task involves more complex contextual information and corresponding relations between terms, as well as longer sequences. These characteristics challenge existing methods that struggle to model explicit span-level interactions or have high computational costs. In this paper, we propose a span-pair interaction and tagging method to solve these issues, which includes a novel Span-pair Tagging Scheme (STS) and a simple and efficient Multi-level Representation Model (MRM). STS simplifies the DiaASQ task to a span-pair tagging task and explicitly captures complete span-level semantics by tagging span pairs. MRM efficiently models the dialogue structure information and span-level interactions by constructing multi-level contextual representation. Besides, we train a span ranker to improve the running efficiency of MRM. Extensive experiments on multilingual datasets demonstrate that our method outperforms existing state-of-the-art methods.

Pre-trained Language Models (PLMs) which are trained on large text corpus via self-supervised learning method, have yielded promising performance on various tasks in Natural Language Processing (NLP). However, though PLMs with huge parameters can effectively possess rich knowledge learned from massive training text and benefit downstream tasks at the fine-tuning stage, they still have some limitations such as poor reasoning ability due to the lack of external knowledge. Research has been dedicated to incorporating knowledge into PLMs to tackle these issues. In this paper, we present a comprehensive review of Knowledge Enhanced Pre-trained Language Models (KE-PLMs) to provide a clear insight into this thriving field. We introduce appropriate taxonomies respectively for Natural Language Understanding (NLU) and Natural Language Generation (NLG) to highlight these two main tasks of NLP. For NLU, we divide the types of knowledge into four categories: linguistic knowledge, text knowledge, knowledge graph (KG), and rule knowledge. The KE-PLMs for NLG are categorized into KG-based and retrieval-based methods. Finally, we point out some promising future directions of KE-PLMs.

Text response generation for multimodal task-oriented dialog systems, which aims to generate the proper text response given the multimodal context, is an essential yet challenging task. Although existing efforts have achieved compelling success, they still suffer from two pivotal limitations: (1) overlook the benefit of generative pretraining and (2) ignore the textual context-related knowledge . To address these limitations, we propose a novel dual knowledge-enhanced generative pretrained language mode for multimodal task-oriented dialog systems (DKMD), consisting of three key components: dual knowledge selection , dual knowledge-enhanced context learning , and knowledge-enhanced response generation . To be specific, the dual knowledge selection component aims to select the related knowledge according to both textual and visual modalities of the given context. Thereafter, the dual knowledge-enhanced context learning component targets seamlessly, integrating the selected knowledge into the multimodal context learning from both global and local perspectives, where the cross-modal semantic relation is also explored. Moreover, the knowledge-enhanced response generation component comprises a revised BART decoder, where an additional dot-product knowledge-decoder attention sub-layer is introduced for explicitly utilizing the knowledge to advance the text response generation. Extensive experiments on a public dataset verify the superiority of the proposed DKMD over state-of-the-art competitors.

Multi-modal fake news detection has drawn considerable attention with the development of online social media. Existing methods primarily conduct direct cross-modal fusion, while ignoring the image-text matching degree which may introduce unexpected bias. This work studies an unexplored problem in multi-modal fake news detection – how to deconfound and leverage the image-text matching bias to improve the performance of fake news detection. The key lies in two aspects: how to remove the confounding effect of the image-text matching bias during training, and how to utilize the bias in the inference stage since the news with mismatched image and text is more likely to be fake. To achieve our goal, we formulate the fake news detection task as a causal graph that reflects the cause-effect factors, and propose a novel framework –Causal Inference forLeveragingImage-textMatchingBias (CLIMB) in multi-modal fake news detection. To our best knowledge, this is the first work that considers the image-text matching degree into the fake news detection task with the approach of causal inference. CLIMB can be applied to any fake news detection models with visual and textual features as inputs. Extensive experiments on two real-world datasets validate the effectiveness of CLIMB.

Heterogeneous information network (HIN) embedding aims to learn the low-dimensional representations of nodes while preserving structures and semantics in HINs. Although most existing methods consider heterogeneous relations and achieve promising performance, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of HIN embedding. In this paper, we argue that heterogeneous relations have different structural characteristics, and propose a novel Relation structure-aware HIN Embedding model, called RHINE. By exploring four real-world networks with thorough analysis, we present two structure-related measures which consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive structural characteristics of relations, in RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures in HINs. Finally, we combine and optimize these models in a unified manner. Furthermore, considering that nodes connected via heterogeneous relations may have multi-aspect semantics and each relation focuses on one aspect, we introduce relation-specific projection matrices to learn node and relation embeddings in separate spaces rather than a common space, which can better preserve the semantics in HINs, referring to a new model RHINE-M. Experiments on four real-world datasets demonstrate that our models significantly outperform the state-of-the-art methods in four tasks.

With the booming of the internet, a popular recommendation scenario has played a vital role in information acquisition for user where the latent heterogeneous collaborative signals and sequential patterns underlying a user's historical behaviors are important for better inferring which item she prefers to interact with next time. Traditional heterogeneous information network based methods or sequential recommendation methods either consider only heterogeneous collaborative signals in the interactions or model user embedding based on only their own item interaction sequence, which either can hardly capture a user's dynamic preferences or face a common data sparsity problem. In this paper, we propose a novel Sequence-aware Heterogeneous graph neural Collaborative Filtering model, called SHCF, which can address the above problems by considering both the high-order heterogeneous collaborative signals and sequential information. Specifically, we first construct a heterogeneous information network (HIN) by enriching the user-item bipartite graph with additional attribute information, and then design novel message passing layers for learning user and item embedding. For user embedding, we consider the sequential information to capture user's dynamic interests over time with a position-aware self-attention mechanism, and capture user's fine-grained static preferences on different aspects of an item with an element-wise attention mechanism. For item embedding, we carefully incorporate the heterogeneous attribute information with dual-level attention, which alleviates the data sparsity problem. Extensive experiments on three real-world datasets illustrate that our model can improve the recommendation performance compared with the state-of-the-art methods.

Short text classification has been widely explored in news tagging to provide more efficient search strategies and more effective search results for information retrieval. However, most existing studies, concentrating on long text classification, deliver unsatisfactory performance on short texts due to the sparsity issue and the insufficiency of labeled data. In this article, we propose a novel heterogeneous graph neural network-based method for semi-supervised short text classification, leveraging full advantage of limited labeled data and large unlabeled data through information propagation along the graph. Specifically, we first present a flexible heterogeneous information network (HIN) framework for modeling short texts, which can integrate any type of additional information and meanwhile capture their relations to address the semantic sparsity. Then, we propose Heterogeneous Graph Attention networks (HGAT) to embed the HIN for short text classification based on a dual-level attention mechanism, including node-level and type-level attentions. To efficiently classify new coming texts that do not previously exist in the HIN, we extend our model HGAT for inductive learning, avoiding re-training the model on the evolving HIN. Extensive experiments on single-/multi-label classification demonstrates that our proposed model HGAT significantly outperforms state-of-the-art methods across the benchmark datasets under both transductive and inductive learning.

With the great popularity of Graph Neural Networks (GNNs), the robustness of GNNs to adversarial attacks has received increasing attention. However, existing works neglect adversarial label-flipping attacks, where the attacker can manipulate an unnoticeable fraction of training labels. Exploring the robustness of GNNs to label-flipping attacks is highly critical, especially when labels are collected from external sources and false labels are easy to inject (e.g., recommendation systems). In this work, we introduce the first study of adversarial label-flipping attacks on GNNs. We propose an effective attack model LafAK based on approximated closed form of GNNs and continuous surrogate of non-differentiable objective, efficiently generating attacks via gradient-based optimizers. Furthermore, we show that one key reason for the vulnerability of GNNs to label-flipping attack is overfitting to flipped nodes. Based on this observation, we propose a defense framework which introduces a community-preserving self-supervised task as regularization to avoid overfitting. We demonstrate the effectiveness of our proposed attack model to GNNs on four real-world datasets. The effectiveness of our defense framework is also well validated by the substantial improvements of defense based GNN and its variants under label-flipping attacks.

With the prevalence of mobile e-commerce nowadays, a new type of recommendation services, called intent recommendation, is widely used in many mobile e-commerce Apps, such as Taobao and Amazon. Different from traditional query recommendation and item recommendation, intent recommendation is to automatically recommend user intent according to user historical behaviors without any input when users open the App. Intent recommendation becomes very popular in the past two years, because of revealing user latent intents and avoiding tedious input in mobile phones. Existing methods used in industry usually need laboring feature engineering. Moreover, they only utilize attribute and statistic information of users and queries, and fail to take full advantage of rich interaction information in intent recommendation, which may result in limited performances. In this paper, we propose to model the complex objects and rich interactions in intent recommendation as a Heterogeneous Information Network. Furthermore, we present a novel M etapath-guided E mbedding method for I ntent Rec ommendation~(called MEIRec). In order to fully utilize rich structural information, we design a metapath-guided heterogeneous Graph Neural Network to learn the embeddings of objects in intent recommendation. In addition, in order to alleviate huge learning parameters in embeddings, we propose a uniform term embedding mechanism, in which embeddings of objects are made up with the same term embedding space. Offline experiments on real large-scale data show the superior performance of the proposed MEIRec, compared to representative methods.Moreover, the results of online experiments on Taobao e-commerce platform show that MEIRec not only gains a performance improvement of 1.54% on CTR metric, but also attracts up to 2.66% of new users to search queries.