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Jipeng Qiang

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AIJ Journal 2026 Journal Article

Analyzing bullet chats for recommendation intent identification: Dataset and method

  • Yi Zhu
  • Qinqin Han
  • Yunhao Yuan
  • Chaowei Zhang
  • Jipeng Qiang
  • Xindong Wu

Live-streaming sales (LS) have emerged as a major e-commerce model particularly in China, with bullet chats serving as the primary channel for audience interaction. Bullet chats refer to real-time scrolling comments, these short, fast-flowing messages create unique analytical challenges, particularly for sellers who must identify and respond to key messages promptly. In this paper, we present the first investigation on bullet chats for live-streaming sales, with a focus on the critical task of Recommendation Intent Identification (RII), i. e. , determining whether a bullet chat reflects a recommendation-seeking intent or is merely casual commentary. Specifically, we construct and release the first publicly available benchmark dataset for RII, termed BC4RII, which comprises 0. 14 million bullet chats collected from four mainstream LS platforms. Furthermore, we propose SPT-RII, an advanced method on the ground of soft prompt-tuning tailored for the RII task. SPT-RII enriches sparse bullet-chat semantics through explanation generation and adapts to streaming input via a dynamic vocabulary update mechanism. Experimental results demonstrate that our method significantly outperforms state-of-the-art baselines, including large language models. To the best of our knowledge, this is the first comprehensive study analyzing bullet chats for RII on live-streaming sales, which establishes a foundational resource and method for real-time intent understanding in live-streaming sales.

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EAAI Journal 2026 Journal Article

LLM4CGDS: Large language model-based agents for Chinese graded document simplification

  • Dengzhao Fang
  • Jipeng Qiang
  • Wenjie Hou
  • Yi Zhu
  • Jingtong Gao
  • Xiangyu Zhao

Graded reading tailors text difficulty to learners’ proficiency by producing multiple versions of the same content—an approach long embraced in language education but still dependent on labor-intensive, expert-driven adaptation. In this paper, we introduce the task of Chinese Graded Document Simplification (CGDS) for non-native learners, which seeks to automate the creation of multi-level reading materials in accordance with established proficiency standards. Guided by the three stages of the Hanyu Shuiping Kaoshi (HSK) 3. 0 framework (Levels 1–3 for Advanced, Levels 4–6 for Intermediate, and Levels 7–9 for Beginner learners), we propose Large Language Model for Chinese Graded Document Simplification (LLM4CGDS), a rule-guided, large language model (LLM)-based framework that integrates HSK-level readability constraints and external knowledge retrieval to control document-level simplification without requiring supervised fine-tuning. To foster further research, we construct two complementary datasets: Journey to the West Document Simplification (JWDS) and Multi-Domain Document Simplification (MDDS) that covering diverse genres and difficulty levels. Experimental evaluation on two datasets demonstrates that LLM4CGDS substantially outperforms direct prompting of state-of-the-art LLMs in both readability control and meaning preservation.

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EAAI Journal 2026 Journal Article

SubAttack: A word-level adversarial textual attack method via antonym substitution

  • Chenqi Hua
  • Xiaojian Liu
  • Yi Zhu
  • Chaowei Zhang
  • Yun Li
  • Yunhao Yuan
  • Jipeng Qiang

Over the past few years, various word-level textual attack approaches have been proposed to reveal the vulnerability in existing deep neural networks and even large language models (LLMs) for Natural Language Processing (NLP). The textual attack aims to fool existing models into making erroneous predictions by altering the text without affecting the user’s understanding. However, current methods either struggle to construct semantically preserved adversarial texts and altered the semantics of the original text, or fail to consider the semantic perturbation constraints and are prone to invalid adversarial examples. In this paper, we propose an efficient and effective framework SubAttack to address these issues. SubAttack is a word-level adversarial textual attack method via antonym substitution, which replaces semantic indicator keywords to generate high-quality adversarial samples with considering both semantically preservation and semantic perturbation. Specifically, the process first involves tokenizing the text and performing part-of-speech tagging Identifying the semantic indicator keywords. Then, the antonym ranking is designed to decide the substitutions of candidate words to fit the context. Finally, while retaining the original text, the ranked antonyms are integrated into the text and the instructions are added for both semantically preservation and semantic perturbation. Extensive experiments reveal that state-of-the-art (SOTA) LLMs (e. g. Llama and QWen) are still vulnerable to our SubAttack. Further experiments show that the adversarial examples crafted by SubAttack usually have higher quality, exhibit better fluency and barely affect human performance and can bring more robustness improvement to victim models by adversarial training.

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EAAI Journal 2025 Journal Article

A domain adaptation method to Defend Chinese textual adversarial attacks via prompt-tuning

  • Yi Zhu
  • Zhenglong Li
  • Yun Li
  • Yunhao Yuan
  • Jipeng Qiang

The textual adversarial attack aims to fool existing models into making erroneous predictions by adding strategic perturbations to normal data without affecting the user’s understanding. Recently, methods based on Pre-trained Language Models (PLMs) and Large Language Models (LLMs) have shown promising performance in various Natural Language Processing (NLP) downstream tasks. However, due to significant deviations between the original and perturbed texts, these methods struggle to achieve satisfactory results in defending against textual adversarial attacks, especially in Chinese, which has unique syntactic structures. To address this issue, we propose a domain adaptation method for defending against Chinese textual adversarial attacks through a prompt-tuning model, which effectively mitigates the discrepancy between different domains. Specifically, the original and perturbed texts are treated as the source and target domains, respectively, with the textual adversarial defense task framed as a cross-domain classification problem. The soft prompt-tuning model trained in the source domain is iteratively adapted to uncover the true label information in the target domain. The graph attention network is incorporated to integrate Chinese syntactic structure information with semantic features. Through a voting mechanism on predicted labels generated by the iterative model, soft prompt-tuning is further optimized for cross-domain classification tasks. Extensive experimental results demonstrate the superior effectiveness of our method in Chinese textual adversarial defense tasks compared to baseline methods, including the state-of-the-art fine-tuning approaches for PLMs and LLMs.

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AAAI Conference 2025 Conference Paper

Is LLMs Hallucination Usable? LLM-based Negative Reasoning for Fake News Detection

  • Chaowei Zhang
  • Zongling Feng
  • Zewei Zhang
  • Jipeng Qiang
  • Guandong Xu
  • Yun Li

The questionable responses caused by knowledge hallucination may lead to LLMs' unstable ability in decision-making. However, it has never been investigated whether the LLMs' hallucination is possibly usable for generating negative reasoning to assist fake news detection. In this paper, we propose a novel supervised self-reinforced reasoning rectification approach - SR^3 that not only yields common reasonable reasoning for news but also forces LLMs to generate the wrong understandings of news via LLMs reflection for semantic consistency learning. Upon that, we construct a negative reasoning-based news learning model called - NRFE, which leverages positive or negative news-reasoning pairs for learning the semantic consistency between them. To avoid the impact of label-implicated reasoning, we deploy a student model - NRFE-D that only takes news content as input to inspect the performance of our method by distilling the knowledge from NRFE. The experimental results verified on three popular fake news datasets demonstrate the superiority of our method compared with three kinds of baselines including prompting-based LLMs, fine-tuning-based PLMs, and other representative fake news detection methods.

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EAAI Journal 2025 Journal Article

Soft Prompt-tuning with Self-Resource Verbalizer for short text streams

  • Yi Zhu
  • Ye Wang
  • Yun Li
  • Jipeng Qiang
  • Yunhao Yuan

Short text streams such as real-time news and search snippets have attained vast amounts of attention and research in recent decades, the characteristics of high generation velocity, feature sparsity, and high ambiguity accentuate both the importance and challenges to language models. However, most of the existing short text stream classification methods can neither automatically select relevant knowledge components for arbitrary samples, nor expand knowledge internally instead of rely on external open knowledge base to address the inherent limitations of short text stream. In this paper, we propose a Soft Prompt-tuning with Self-Resource Verbalizer (SPSV for short) for short text stream classification, the soft prompt with self-resource knowledgeable expansion is conducted for updating label words space to address evolved semantic topics in the data streams. Specifically, the automatic constructed prompt is first generated to instruct the model prediction, which is optimized to address the problem of high velocity and topic drift in short text streams. Then, in each chunk, the projection between category names and label words space, i. e. verbalizer, is updated, which is constructed by internal knowledge expansion from the short text itself. Through comprehensive experiments on four well-known benchmark datasets, we validate the superb performance of our method compared to other short text stream classification and fine-tuning PLMs methods, which achieves up to more than 90% classification accuracy with the counts of data chunk increased.

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ECAI Conference 2023 Conference Paper

Multilingual Lexical Simplification via Paraphrase Generation

  • Kang Liu
  • Jipeng Qiang
  • Yun Li 0010
  • Yunhao Yuan 0001
  • Yi Zhu 0006
  • Kaixun Hua

Lexical simplification (LS) methods based on pretrained language models have made remarkable progress, generating potential substitutes for a complex word through analysis of its contextual surroundings. However, these methods require separate pretrained models for different languages and disregard the preservation of sentence meaning. In this paper, we propose a novel multilingual LS method via paraphrase generation, as paraphrases provide diversity in word selection while preserving the sentence’s meaning. We regard paraphrasing as a zero-shot translation task within multilingual neural machine translation that supports hundreds of languages. After feeding the input sentence into the encoder of paraphrase modeling, we generate the substitutes based on a novel decoding strategy that concentrates solely on the lexical variations of the complex word. Experimental results demonstrate that our approach surpasses BERT-based methods and zero-shot GPT3-based method significantly on English, Spanish, and Portuguese.

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AIJ Journal 2023 Journal Article

Natural language watermarking via paraphraser-based lexical substitution

  • Jipeng Qiang
  • Shiyu Zhu
  • Yun Li
  • Yi Zhu
  • Yunhao Yuan
  • Xindong Wu

Although powerful pretrained language models generate high-quality output text, they bring new concerns about the potential misuse of such models for malicious purposes. Natural language watermarking (NLW) is a technique that is desgined to help tracing the provenance of texts for againsting possible attacks, where the watermark signals are embedded into cover texts using synonym substitutions. The up-to-date BERT-based NLW methods have made remarkable progress on performance improvement of watermarking through generating substitutes for a masked target word. Yet, the BERT-based NLWs focus on the context of texts rather than the meaning of target words, which might make the capacity of watermark embeddings being lower. To address the limitations, this study proposes a novel NLW method by incorporating a paraphraser-based lexical substitution method. Under the promise of paraphrase preservation, the proposed NLW method utilizes the knowledge of paraphrase modeling to generate the substitute candidates to replace the words in original sentences capable of carrying the watermark signal in local contexts. We empirically show that our NLW method not only has a better meaning-preserved, but improves the payload more than 2 times compared with the BERT-based NLW method. Besides, compared with previous state-of-the-art method Compared with other state-of-the-art baselines, the experimental results show that the proposed LS method improves the Precision@1 score from 51. 7% to 58. 3% and from 50. 5% to 62. 6% on LS07 and CoInCo benchmarks, respectively.

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AAAI Conference 2020 Conference Paper

Lexical Simplification with Pretrained Encoders

  • Jipeng Qiang
  • Yun Li
  • Yi Zhu
  • Yunhao Yuan
  • Xindong Wu

Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably produce a large number of spurious candidates. We present a simple LS approach that makes use of the Bidirectional Encoder Representations from Transformers (BERT) which can consider both the given sentence and the complex word during generating candidate substitutions for the complex word. Specifically, we mask the complex word of the original sentence for feeding into the BERT to predict the masked token. The predicted results will be used as candidate substitutions. Despite being entirely unsupervised, experimental results show that our approach obtains obvious improvement compared with these baselines leveraging linguistic databases and parallel corpus, outperforming the state-of-the-art by more than 12 Accuracy points on three well-known benchmarks.

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AAAI Conference 2016 Conference Paper

Text Simplification Using Neural Machine Translation

  • Tong Wang
  • Ping Chen
  • John Rochford
  • Jipeng Qiang

Text simplification (TS) is the technique of reducing the lexical, syntactical complexity of text. Existing automatic TS systems can simplify text only by lexical simplification or by manually defined rules. Neural Machine Translation (NMT) is a recently proposed approach for Machine Translation (MT) that is receiving a lot of research interest. In this paper, we regard original English and simplified English as two languages, and apply a NMT model–Recurrent Neural Network (RNN) encoder-decoder on TS to make the neural network to learn text simplification rules by itself. Then we discuss challenges and strategies about how to apply a NMT model to the task of text simplification.

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