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Lianwei Wu

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10 papers

2 author rows

AAAI Conference 2026 Conference Paper

Cognitive Enhancement Chain-of-Thought Towards Enhancing Style Learning and Content Preservation for Long Style Transfer

Lianwei Wu
Botao Wang
Wenbo An
Tieqiao Li
Xianghua Li

Current text style transfer task mainly focuses on short texts, while the field has not been fully developed for long texts. Considering the richer semantics and more complex sentence structures in long text sequences, existing methods that employ traditional style-content disentanglement ways and learn the target style to generate target sequences face two key issues: 1) During disentanglement, they usually directly separate style words or fragments, such coarse-grained disentanglement risks losing original semantics and hinder the model's content preservation. 2) During target style learning, they often focus on the transfer of certain style attributes or aspects, which makes it difficult to grasp the holistic style of target objects. To this end, we propose Cognitive enhancement Chain-of-Thought (CeCoT) towards enhancing style learning and content preservation for long style transfer. CeCoT first constructs progressive CoT to facilitate LLMs to gradually rewrite source content and separate source styles, thereby enhancing the retention of original content. Then, we propose cognitive CoT, which comprehensively considers hierarchical cognitive content (i.e., shallower-deeper-normal level) and cognitive behavior (i.e., prompt order of CoT) to learn the overall target style. To enhance the robustness of our model, we also propose two constraint losses in a dual validation way towards content preservation enhancing and style consistency learning. Extensive experiments on two competitive datasets demonstrate the superiority of our CeCoT.

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AAAI Conference 2026 Conference Paper

Multi-level Style Preference Optimization: An Adaptive Detection Framework for Human-Machine Hybrid Text

Zehao Wang
Lianwei Wu
Wenbo An
Hang Zhang
Yaxiong Wang

Large language model (LLM) generated texts now rival human quality, creating four text categories: purely machine-generated, machine-rewritten, machine-polished, and human-written content. Traditional detection methods face significant challenges in human-machine hybrid scenarios where LLMs perform rewriting or polishing, as existing approaches focus on single-level features and fail to capture subtle, multi-layered machine traces. To address this, we propose the Multi-level Style Preference Optimization (MSPO) framework, capturing machine style features at multiple granularities: sequence-level (overall consistency), phrase-level (distinctive n-gram patterns), and lexical-level (word selection distributions). We further incorporate four text complexity indicators (Type-Token Ratio, Average Sentence Length, Average Word Length, and Punctuation Ratio) to dynamically adjust optimization parameters based on human-machine text complexity differences, enhancing adaptability across diverse text types. Additionally, we construct a comprehensive detection dataset spanning three representative domains (scientific writing, news articles, and creative writing) across four text types (human-written, purely machine-generated, machine-rewritten, and machine-polished), generated using state-of-the-art LLMs for robust evaluation. Experimental results demonstrate that MSPO significantly outperforms existing methods across all text types. On the challenging rewritten texts, MSPO achieves up to 82.14% AUROC, representing an improvement of 11.15 percentage points over the strongest baseline ImBD, while maintaining robust cross-domain generalizability across scientific, news, and creative writing domains.

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IJCAI Conference 2025 Conference Paper

A Prior-based Discrete Diffusion Model for Social Graph Generation

Shu Yin
Dongpeng Hou
Lianwei Wu
Xianghua Li
Chao Gao

Graph generation is essential in social network analysis, particularly for modeling information flow and user interactions. However, existing probabilistic diffusion models face challenges when applied to social propagation graphs. The continuous noise does not apply to the discrete nature of graph generation tasks, and the random Gaussian initialization in the reverse process can introduce biases that deviate from real-world propagation patterns. To address these issues, this paper introduces a Prior-based Discrete Diffusion Model (PDDM) for social graph generation. PDDM redefines the forward process as a discrete process for node denoising and edge generation, and the task of the denoising module is transformed into the connection probability learning of node-level tasks. Further, PDDM employs a new starting point of the reverse process by incorporating user similarity as the probability matrix, which can better leverage the social context. These developments mitigate reverse-starting bias and enhance model robustness. Moreover, PDDM integrates lightweight deep graph networks such as GAT, demonstrating both scalability and applicability to graph generation scenarios. Comprehensive experiments on real-world social network datasets demonstrate PDDM’s superiority in terms of the MMD metric and downstream tasks. The code is available at https: //github. com/cgao-comp/PDDM.

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AAAI Conference 2025 Conference Paper

Distilling Structured Rationale from Large Language Models to Small Language Models for Abstractive Summarization

Linyong Wang
Lianwei Wu
Shaoqi Song
Yaxiong Wang
Cuiyun Gao
Kang Wang

Large Language Models (LLMs) have permeated various Natural Language Processing (NLP) tasks. For the summarization tasks, LLMs can generate well-structured rationales, which consist of Essential Aspects (EA), Associated Sentences (AS) and Triple Entity Relations (TER). These rationales guide smaller models (≤1B) to produce better summaries. However, their high deployment costs (≥70B), such as substantial storage space and high computing requirements, limit their utilization in resource-constrained environments. Furthermore, effectively distilling these structured rationales from LLMs into Small Language Models (SLMs) models remains a challenge. To address this, we propose the LLM-based Structured Rationale-guided Multi-view Weak-gated Fusion framework (LSR-MWF). The framework initially employs LLMs to dig structural rationales from a document, considering multiple viewpoints such as EA, AS, and TER. Then, it develop a multi-step summary generation evaluation strategy to select high-quality structured rationales. Subsequently, it aligns with these rationales using additional modules organized in a hierarchical structure. Finally, the framework integrates the features output by these modules with original abstractive model through a weak-gated mechanism. Experimental results on two publicly available CNN/DailyMail and XSum datasets show that our method improves the performance of the abstractive model, outperforming baselines by 11.2％ and 5.8％, respectively. In addition, our method improves the interpretability of summary generation from the viewpoints of EA, AS and TER.

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AAAI Conference 2024 Conference Paper

GAMC: An Unsupervised Method for Fake News Detection Using Graph Autoencoder with Masking

Shu Yin
Peican Zhu
Lianwei Wu
Chao Gao
Zhen Wang

With the rise of social media, the spread of fake news has become a significant concern, potentially misleading public perceptions and impacting social stability. Although deep learning methods like CNNs, RNNs, and Transformer-based models like BERT have enhanced fake news detection. However, they primarily focus on content and do not consider social context during news propagation. Graph-based techniques have incorporated the social context but are limited by the need for large labeled datasets. To address these challenges, this paper introduces GAMC, an unsupervised fake news detection technique using the Graph Autoencoder with Masking and Contrastive learning. By leveraging both the context and content of news propagation as self-supervised signals, our method reduces the dependency on labeled datasets. Specifically, GAMC begins by applying data augmentation to the original news propagation graphs. Subsequently, these augmented graphs are encoded using a graph encoder and subsequently reconstructed via a graph decoder. Finally, a composite loss function that encompasses both reconstruction error and contrastive loss is designed. Firstly, it ensures the model can effectively capture the latent features, based on minimizing the discrepancy between reconstructed and original graph representations. Secondly, it aligns the representations of augmented graphs that originate from the same source. Experiments on the real-world dataset validate the effectiveness of our method.

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IJCAI Conference 2024 Conference Paper

Unified Evidence Enhancement Inference Framework for Fake News Detection

Lianwei Wu
Linyong Wang
Yongqiang Zhao

The current approaches for fake news detection are mainly devoted to extracting candidate evidence from comments (or external articles) and establishing interactive reasoning with the news itself to verify the falsehood of the news. However, they still have several drawbacks: 1) The interaction object is coarse-grained, which mainly drives the entire news to participate in interaction, but ignores the learning of potential suspicious segments in news; 2) The reasoning ways are relatively single, making it difficult to explore the various possible correlations between news and candidate evidence. To this end, we propose Unified Evidence Enhancement Inference framework (UEEI) to discover and infer high-quality evidence to reveal the false parts of news for detection. Specifically, UEEI first promotes the interaction fusion between comments and news from the perspectives of semantic and emotion, thereby learning potential suspicious fragments in news. Then, the model constructs entity-level and relationship-level retrievals to screen sufficient candidate evidence from external sources. Finally, we measure coherence between suspicious fragments and candidate evidence by multi-view reasoning, and further infer explainable evidence that discovers the false parts of news. Experiments on three public datasets confirm the effectiveness and interpretability of our UEEI.

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AAAI Conference 2023 Conference Paper

See How You Read? Multi-Reading Habits Fusion Reasoning for Multi-Modal Fake News Detection

Lianwei Wu
Pusheng Liu
Yanning Zhang

The existing approaches based on different neural networks automatically capture and fuse the multimodal semantics of news, which have achieved great success for fake news detection. However, they still suffer from the limitations of both shallow fusion of multimodal features and less attention to the inconsistency between different modalities. To overcome them, we propose multi-reading habits fusion reasoning networks (MRHFR) for multi-modal fake news detection. In MRHFR, inspired by people's different reading habits for multimodal news, we summarize three basic cognitive reading habits and put forward cognition-aware fusion layer to learn the dependencies between multimodal features of news, so as to deepen their semantic-level integration. To explore the inconsistency of different modalities of news, we develop coherence constraint reasoning layer from two perspectives, which first measures the semantic consistency between the comments and different modal features of the news, and then probes the semantic deviation caused by unimodal features to the multimodal news content through constraint strategy. Experiments on two public datasets not only demonstrate that MRHFR not only achieves the excellent performance but also provides a new paradigm for capturing inconsistencies between multi-modal news.

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AAAI Conference 2021 Conference Paper

Evidence Inference Networks for Interpretable Claim Verification

Lianwei Wu
Yuan Rao
Ling Sun
Wangbo He

Existing approaches construct appropriate interaction models to explore semantic conflicts between claims and relevant articles, which provides practical solutions for interpretable claim verification. However, these conflicts are not necessarily all about questioning the false part of claims, which makes considerable semantic conflicts difficult to be used as evidence to explain the results of claim verification, especially those that cannot identify the core semantics of claims. In this paper, we propose evidence inference networks (EVIN), which focus on the conflicts questioning the core semantics of claims and serve as evidence for interpretable claim verification. Specifically, EVIN first captures the core semantic segments of claims and the users’ principal opinions in relevant articles. Then, it finely-grained identifies the semantic conflicts contained in each relevant article from these opinions. Finally, EVIN constructs coherence modeling to match the conflicts that queries the core semantic fragments of claims as explainable evidence. Experiments on two widely used datasets demonstrate that EVIN not only achieves satisfactory performance but also provides explainable evidence for end-users.

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ECAI Conference 2020 Conference Paper

Adaptive Interaction Fusion Networks for Fake News Detection

Lianwei Wu
Yuan Rao 0004

The majority of existing methods for fake news detection universally focus on learning and fusing various features for detection. However, the learning of various features is independent, which leads to a lack of cross-interaction fusion between features on social media, especially between posts and comments. Generally, in fake news, there are emotional associations and semantic conflicts between posts and comments. How to represent and fuse the cross-interaction between both is a key challenge. In this paper, we propose Adaptive Interaction Fusion Networks (AIFN) to fulfill cross-interaction fusion among features for fake news detection. In AIFN, to discover semantic conflicts, we design gated adaptive interaction networks (GAIN) to capture adaptively similar semantics and conflicting semantics between posts and comments. To establish feature associations, we devise semantic-level fusion self-attention networks (SFSN) to enhance semantic correlations and fusion among features. Extensive experiments on two real-world datasets, i. e. , RumourEval and PHEME, demonstrate that AIFN achieves the state-of-the-art performance and boosts accuracy by more than 2. 05% and 1. 90%, respectively.

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IJCAI Conference 2020 Conference Paper

Evidence-Aware Hierarchical Interactive Attention Networks for Explainable Claim Verification

Lianwei Wu
Yuan Rao
Xiong Yang
Wanzhen Wang
Ambreen Nazir

Exploring evidence from relevant articles to confirm the veracity of claims is a trend towards explainable claim verification. However, most strategies capture the top-k check-worthy articles or salient words as evidence, but this evidence is difficult to focus on the questionable parts of unverified claims. Besides, they utilize relevant articles indiscriminately, ignoring the source credibility of these articles, which may cause quiet a few unreliable articles to interfere with the assessment results. In this paper, we propose Evidence-aware Hierarchical Interactive Attention Networks (EHIAN) by considering the capture of evidence fragments and the fusion of source credibility to explore more credible evidence semantics discussing the questionable parts of claims for explainable claim verification. EHIAN first designs internal interaction layer (IIL) to strengthen deep interaction and matching between claims and relevant articles for obtaining key evidence fragments, and then proposes global inference layer (GIL) that fuses source features of articles and interacts globally with the average semantics of all articles and finally earns the more credible evidence semantics discussing the questionable parts of claims. Experiments on two datasets demonstrate that EHIAN not only achieves the state-of-the-art performance but also secures effective evidence to explain the results.

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