Papers by Jin Cui
CHIFRAUD: A Long-term Web Text Dataset for Chinese Fraud Detection (2025.coling-main)
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| Challenge: | Detecting fraudulent online text is essential as they exploit human greed and deceive individuals. |
| Approach: | They propose to build a long-term dataset of Chinese fraudulent texts collected over 12 months. |
| Outcome: | The proposed dataset includes 59,106 entries extracted from billions of web pages and includes large language model-based detectors and pre-trained language model approaches. |
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data (2024.emnlp-industry)
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Haoran Sun, Renren Jin, Shaoyang Xu, Leiyu Pan, null Supryadi, Menglong Cui, Jiangcun Du, Yikun Lei, Lei Yang, Ling Shi, Juesi Xiao, Shaolin Zhu, Deyi Xiong
| Challenge: | Large language models exhibit significant performance discrepancies between high- and low-resource languages. |
| Approach: | They present an open-source multilingual LLM with 8 billion parameters and a multilingual instruction dataset. |
| Outcome: | The proposed model achieves consistent multilingual representations across languages. |
CodeArena: Evaluating and Aligning CodeLLMs on Human Preference (2025.emnlp-main)
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Jian Yang, Jiaxi Yang, Wei Zhang, Jin Ke, Yibo Miao, Lei Zhang, Liqun Yang, Zeyu Cui, Yichang Zhang, Zhoujun Li, Binyuan Hui, Junyang Lin
| Challenge: | Code large language models (codeLLMs) focus on synthesizing the correct code snippet, ignoring the alignment with human preferences. |
| Approach: | They propose a benchmark code-based on 40 categories and 44 programming languages to emulate real-world coding tasks. |
| Outcome: | The proposed benchmarks show that open-source code LLMs perform better than open-sourced ones. |
TITA: A Two-stage Interaction and Topic-Aware Text Matching Model (2021.naacl-main)
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| Challenge: | Existing models for keyword-document matching do not define topic-aware relevance clearly. |
| Approach: | They propose a two-stage interaction and topic-aware text matching model to solve this problem . they propose to combine latent topic of document with deep neural representation to model complex interactions between keyword and document. |
| Outcome: | The proposed model outperforms other well-designed baselines and shows excellent performance in the recommendation system. |
FinMaster: A Holistic Benchmark for Full-Pipeline Financial Management with Large Language Models (2026.findings-acl)
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Junzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin, Yujing Zhang, Yilin Xiao, Ruiyu Wang, Bo Li, Xiao Huang, Danny Dongning Sun, Xinrun Wang
| Challenge: | Existing benchmarks lack domain-specific data, realistic workflow-level task design, and standardized workflow- level evaluation. |
| Approach: | a new benchmark evaluates large language models on financial management workflows . the global financial services market is projected to grow to $37 trillion by 2027 . |
| Outcome: | a new benchmark for large language models on financial management workflows reveals critical capability gaps . accuracy drops from 90% on basic tasks to 40% on complex scenarios requiring multi-step reasoning . the global financial services market reached $25.8 trillion in 2022 and is projected to grow to $37 trillion by 2027 . |
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety (2024.acl-demos)
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Chuang Liu, Linhao Yu, Jiaxuan Li, Renren Jin, Yufei Huang, Ling Shi, Junhui Zhang, Xinmeng Ji, Tingting Cui, Liutao Liutao, Jinwang Song, Hongying Zan, Sun Li, Deyi Xiong
| Challenge: | a rapid development of Chinese large language models poses big challenges for efficient LLM evaluation. |
| Approach: | They propose an evaluation testbed that benchmarks Chinese LLMs across capability, alignment and safety. |
| Outcome: | The evaluation platform OpenEval benchmarks Chinese LLMs across capability, alignment and safety. |
MIND: From Passive Mimicry to Active Reasoning through Capability-Aware Multi-Perspective CoT Distillation (2026.acl-long)
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Jin Cui, Jiaqi Guo, Jiepeng Zhou, Ruixuan Yang, Jiayi Lu, Jiajun Xu, Jiangcheng Song, Boran Zhao, Pengju Ren
| Challenge: | Existing approaches restrict students to following a single golden rationale and treat different reasoning paths independently, causing suboptimal performance. |
| Approach: | They propose a capability-adaptive framework that transitions distillation from passive mimicry to active cognitive construction and employ a feedback-driven inertia calibration mechanism to align supervision with the student’s current adaptability. |
| Outcome: | Experiments show that the proposed framework achieves state-of-the-art performance on both in-distribution and out-of distribution benchmarks. |
ADEPT-SQL: A High-performance Text-to-SQL Application for Real-World Enterprise-Level Databases (2025.acl-demo)
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Yongnan Chen, Zhuo Chang, Shijia Gu, Yuanhang Zong, Zhang Mei, Shiyu Wang, Hezixiang Hezixiang, Hongzhi Chen, Jin Wei, Bin Cui
| Challenge: | et al., 2017) address domain-specific knowledge barriers, schemas complexity, and computational costs of large LLMs. |
| Approach: | They propose a domain-adapted Text2SQL system that addresses critical deployment challenges in professional fields. |
| Outcome: | The proposed system achieves 97% execution accuracy on real-world databases . it is faster than existing systems and has a higher performance than existing ones. |
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection (2024.emnlp-main)
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| Challenge: | Existing approaches to visual-language understanding lack unified tokenization for images and videos . lack of unified visual representations makes it difficult to learn multi-modal interactions from poor projection layers. |
| Approach: | They propose to unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM. |
| Outcome: | The proposed model outperforms Video-ChatGPT on image benchmarks and on 9 image benchmark benchmarks. |
Ignore Me But Don’t Replace Me: Utilizing Non-Linguistic Elements for Pretraining on the Cybersecurity Domain (2024.findings-naacl)
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| Challenge: | Existing methods to train domain expertise for cybersecurity text domains are expensive to train and run. |
| Approach: | They propose to use pretraining methods to account for non-linguistic elements in cybersecurity texts and evaluate their effectiveness through downstream tasks and probing tasks. |
| Outcome: | The proposed strategy outperforms the commonly taken approach of replacing NLEs and outperformed other cybersecurity PLMs on most tasks. |
Aspect-Category Enhanced Learning with a Neural Coherence Model for Implicit Sentiment Analysis (2023.findings-emnlp)
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a major research topic in NLP since social networking services have increased . but the recognition of implicit sentiments that do not contain obvious opinion words remains unexplored . elcom captures document-level coherence by using contrastive learning and sentence-level by a hypergraph . |
| Approach: | They propose aspect-category enhanced learning with a neural coherence model . it captures document-level coherency by contrastive learning and sentence-level by a hypergraph . |
| Outcome: | The proposed model captures document-level coherence by using contrastive learning and sentence-level by a hypergraph to mine opinions from explicit sentences to aid implicit sentiment classification. |
The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning (2024.emnlp-main)
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| Challenge: | Despite its significance, a systematic exploration of commonsense causality is lacking. |
| Approach: | They focus on taxonomies, benchmarks, acquisition methods, qualitative reasoning, and quantitative measurements in commonsense causality. |
| Outcome: | The proposed method synthesizes insights from over 200 representative articles and provides a practical guide for beginners. |
Enhancing Document Ranking with Task-adaptive Training and Segmented Token Recovery Mechanism (2021.emnlp-main)
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| Challenge: | Document Retrieval (DR) requires the machine to retrieve and rank documents according to their relevance with the query. |
| Approach: | They propose a ranking model DR-BERT which improves the Document Retrieval task by a task-adaptive training process and a Segmented Token Recovery Mechanism. |
| Outcome: | The proposed ranking model keeps in the top three on the MS MARCO leaderboard since 2020. |
KCVR: Knowledge-Centric Video Reconstruction for Structured Pedagogical Summarization via Dynamic Graph Planning (2026.acl-long)
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Jingjiang Liu, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Xiaokang Jin, Yilin Wang, Qingyu Niu, Jiawei Shen, Guoqing Ma, Yidan Liang, Shimin Di, Jiajie Xu
| Challenge: | Existing summarization methods compress content for gist browsing, but they break prerequisite logic in instructional videos. |
| Approach: | They propose a framework that decouples epistemic planning from content generation. |
| Outcome: | The proposed framework outperforms strong end-to-end baselines on Knowledge Progression Consistency and Learning Objective Coverage. |
Causal Denoising Prototypical Network for Few-Shot Multi-label Aspect Category Detection (2025.findings-acl)
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| Challenge: | Recent methods that learn robust prototypes to represent aspects with limited support samples address noise categories in the support set that hinder their models from effective prototype generation. |
| Approach: | They propose a causal denoising prototypical network for few-shot MACD by learning robust prototypes to represent categories with limited support samples. |
| Outcome: | The proposed model outperforms baseline models and can prevent models from overly predicting more categories and mitigate semantic ambiguity issues among categories. |
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models (2024.findings-acl)
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Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, May Dongmei Wang, Wei Jin, Joyce Ho, Carl Yang
| Challenge: | Clinical natural language processing (NLP) is a subfield that requires the extraction, analysis, and interpretation of unstructured clinical text. |
| Approach: | They propose a model which infuses knowledge into clinical text generation with LLMs for clinical NLP tasks. |
| Outcome: | The proposed model improves performance across 8 clinical NLP tasks and 18 datasets by 7.7%-8.7% on average. |
Enhancing High-order Interaction Awareness in LLM-based Recommender Model (2024.emnlp-main)
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| Challenge: | Existing approaches to model user-item interactions do not account for high-order interactions. |
| Approach: | They propose to enhance whole-word embeddings to enhance LLMs’ interpretation of graph-constructed interactions for recommendations without requiring graph pre-training. |
| Outcome: | The proposed model outperforms state-of-the-art methods in direct recommendations. |
AGRec: Adapting Autoregressive Decoders with Graph Reasoning for LLM-based Sequential Recommendation (2025.findings-acl)
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| Challenge: | Autoregressive decoders in large language models excel at capturing sequential behaviors for generative recommendations, but they lack graph-structured user-item interactions, which are widely recognized as beneficial. |
| Approach: | They propose a novel algorithm that adapts LLMs’ decoders with graph reasoning for recommendation by augmenting the decoding logits with an auxiliary GNN model to optimize token generation. |
| Outcome: | The proposed model outperforms state-of-the-art models in sequential recommendations. |
DarkBERT: A Language Model for the Dark Side of the Internet (2023.acl-long)
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| Challenge: | Recent research suggests that there are clear differences in the language used in the Dark Web compared to that of the Surface Web. |
| Approach: | They propose a language model that is pretrained on Dark Web data to combat extreme lexical diversity. |
| Outcome: | The proposed model outperforms existing models and may be useful for future research on the Dark Web. |
Enhanced Coherence-Aware Network with Hierarchical Disentanglement for Aspect-Category Sentiment Analysis (2024.lrec-main)
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| Challenge: | Aspect-category-based sentiment analysis (ACSA) is a popular approach for identifying aspect categories and predicting their sentiments. |
| Approach: | They propose an enhanced coherence-aware network with hierarchical disentanglement (ECAN) to capture contexts across the whole review and to help the implicit aspect and sentiment identification. |
| Outcome: | The proposed network decouples multiple aspects and sentiment features and achieves state-of-the-art (SOTA) performance. |
RDRec: Rationale Distillation for LLM-based Recommendation (2024.acl-short)
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| Challenge: | Existing models that bridge users and items through textual prompts for effective semantic reasoning do not consider the underlying rationales behind interactions, such as user preferences and item attributes. |
| Approach: | They propose a rationale distillation recommender model that learns rationales generated by a larger language model (LM) by leveraging reviews related to users and items. |
| Outcome: | The proposed model achieves state-of-the-art (SOTA) performance in top-N and sequential recommendations. |
Event-Centric Query Expansion in Web Search (2023.acl-industry)
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| Challenge: | Existing studies rely on long-term search log mining to improve search experience . EQE system is a novel event retrieval framework that can select the best expansion from a significant amount of potential events quickly and accurately. |
| Approach: | They propose a QE system that uses a four-stage event retrieval framework . they collect news headlines and then refine a dual-tower semantic model to serve as an encoder . |
| Outcome: | The proposed system can select the best expansion from a significant amount of potential events quickly and accurately. |