Papers by Weiqi Wang
CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning (2023.acl-long)
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| Challenge: | HKUST-KnowComp proposes a framework for commonsense reasoning that can be used to conceptualize commonsence knowledge bases at scale. |
| Approach: | They propose a framework that integrates event conceptualization and instantiation to conceptualize commonsense knowledge bases at scale. |
| Outcome: | The proposed framework achieves state-of-the-art on two conceptualization tasks and the acquired abstract commonsense knowledge significantly improves commonsence inference modeling. |
MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset (2025.acl-long)
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| Challenge: | Recent advances in LLMs have demonstrated superior performance in a variety of reasoning tasks (Liu et al., 2023b; Chan e t al, 2024; Qin eetal., 2023) However, to truly achieve conscious processing, the integration of System II reasoning ability is essential. |
| Approach: | They propose a three-step process for reasoning with distributional changes, termed as a metaphysical resoning, and propose 'MARS' task to assess LLMs' reasoning abilities. |
| Outcome: | The proposed task is based on a three-step discriminative process and is compared with a standard model with 20 LLMs of varying sizes and methods. |
Cross-Sentence Grammatical Error Correction (P19-1)
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| Challenge: | Existing approaches to automatic grammatical error correction (GEC) ignore cross-sentence context . existing approaches only correct one sentence at a time and ignore useful contextual information . |
| Approach: | They propose to use an auxiliary encoder that encodes previous sentences and incorporates the encoding in the decoder via attention and gating mechanisms. |
| Outcome: | The proposed model improves over strong baselines on a synthetic dataset showing high performance in verb tense corrections that require cross-sentence context. |
arXiv2Table: Toward Realistic Benchmarking and Evaluation for LLM-Based Literature-Review Table Generation (2026.acl-long)
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| Challenge: | Literature review tables are essential for summarizing and comparing collections of scientific papers. |
| Approach: | They propose to generate a database of literature review tables from a pool of papers and to model retrieval noise via semantically related but out-of-scope distractor papers verified by human annotators. |
| Outcome: | The proposed method improves over strong baselines while the absolute scores remain modest, underscoring the task’s difficulty. |
StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding (2023.emnlp-main)
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Cheng Jiayang, Lin Qiu, Tsz Chan, Tianqing Fang, Weiqi Wang, Chunkit Chan, Dongyu Ru, Qipeng Guo, Hongming Zhang, Yangqiu Song, Yue Zhang, Zheng Zhang
| Challenge: | Analogy-making between narratives is crucial for human reasoning . despite its importance, there has been limited research on story analogies . |
| Approach: | They construct a large-scale story-level analogy corpus with 24K story pairs . they find that the tasks are incredibly difficult for large language models such as ChatGPT . |
| Outcome: | The proposed corpus contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory. |
On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions (2025.findings-emnlp)
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Weiqi Wang, Tianqing Fang, Haochen Shi, Baixuan Xu, Wenxuan Ding, Liyu Zhang, Wei Fan, Jiaxin Bai, Haoran Li, Xin Liu, Yangqiu Song
| Challenge: | Conceptualization is a fundamental element of human cognition and plays a pivotal role in generalizable reasoning. |
| Approach: | They propose to categorize different types of conceptualizations into four levels based on the types of instances being conceptualized. |
| Outcome: | The proposed categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized . |
Interpretable Math Word Problem Solution Generation via Step-by-step Planning (2023.acl-long)
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| Challenge: | Existing approaches to solving math word problems focus on obtaining the correct answer. |
| Approach: | They propose a step-by-step planning approach for intermediate solution generation that strategically plans the generation of the next solution step based on the MWP and the previous solution steps. |
| Outcome: | The proposed approach improves the accuracy and interpretability of the solution on automatic metrics and human evaluation. |
Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering (2025.emnlp-main)
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| Challenge: | Misleading visualizations can distort perception and lead to incorrect conclusions. |
| Approach: | They propose a large-scale multimodal dataset to evaluate MLLMs on misleading chart reasoning. |
| Outcome: | The proposed framework evaluates MLLMs on misleading chart reasoning on a large-scale multimodal dataset spanning 21 misleader types and 10 chart types . it contains 3,026 curated examples spanning standard chart code, CSV data, multiple-choice questions, and labeled explanations, validated through iterative MLML checks and exhausted expert human review. |
QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering (2023.findings-emnlp)
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| Challenge: | Existing approaches to QA fine-tune language models on QA pairs constructed from CommonSense Knowledge Bases (CSKBs) however, current QA synthesis protocols introduce noise from the CSKB and generate ungrammatical questions and false negative options, which impede the model’s ability to generalize. |
| Approach: | They propose a framework to analyze the training dynamics of each QA pair at both the question level and option level, discarding machine-detectable artifacts and mislabeled or false-negative options. |
| Outcome: | The proposed framework outperforms baseline approaches while using only 33% of the synthetic data. |
AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph (2024.findings-naacl)
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Zhaowei Wang, Haochen Shi, Weiqi Wang, Tianqing Fang, Hongming Zhang, Sehyun Choi, Xin Liu, Yangqiu Song
| Challenge: | Existing language models only touch nouns or verbs within simplified events or specific domains. |
| Approach: | They propose an entailment graph that collects abstract knowledge for 3 components of diverse events to comprehensively evaluate the abstraction ability of language models. |
| Outcome: | The proposed benchmark improves LLMs across two previous abstraction tasks. |
NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding (2024.findings-emnlp)
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Chunkit Chan, Cheng Jiayang, Yauwai Yim, Zheye Deng, Wei Fan, Haoran Li, Xin Liu, Hongming Zhang, Weiqi Wang, Yangqiu Song
| Challenge: | Theory of mind evaluations currently focus on testing models using machine-generated data or game settings prone to shortcuts and spurious correlations. |
| Approach: | They propose a benchmark to stress-test machine ToM in real-world negotiation surrounding covered multi-dimensional mental states. |
| Outcome: | The proposed benchmark builds upon the Belief-Desire-Intention theory and conducts the necessary empirical experiments to evaluate large language models. |
Controllable Style Arithmetic with Language Models (2025.acl-long)
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| Challenge: | Existing methods for linguistic style control lack fine-grained control, require extensive computation, or introduce significant latency. |
| Approach: | They propose a parameter-space approach that extracts style-specific representations by analyzing parameter differences between models trained on contrasting styles and incorporates them into a model with precise control over style intensity. |
| Outcome: | The proposed approach achieves three key capabilities while achieving optimal computational efficiency. |
KnowShiftQA: How Robust are RAG Systems when Textbook Knowledge Shifts in K-12 Education? (2025.acl-short)
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| Challenge: | Existing knowledge discrepancies between textbooks and large language models can undermine RAG systems' performance. |
| Approach: | They propose to use a dataset to test RAG system robustness against knowledge discrepancies. |
| Outcome: | The proposed dataset shows that RAG systems suffer performance degradation when faced with knowledge discrepancies. |
Dynamic Personality in LLM Agents: A Framework for Evolutionary Modeling and Behavioral Analysis in the Prisoner’s Dilemma (2025.findings-acl)
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| Challenge: | Current models rely on static personality traits but lack natural selection processes and direct psychological metrics, failing to accurately capture authentic dynamic personality variations. |
| Approach: | They propose a framework that uses game payoffs as environmental feedback to drive adaptive personality evolution and analyze correlations between personality metrics and behavior. |
| Outcome: | The proposed framework reveals new behavioral patterns of agents and evaluates personality-behavior relationships, advancing agent-based social simulations and human-AI symbiosis research. |
InferenceDynamics: Adaptive LLM Routing through Structured Capability and Knowledge Profiling (2026.acl-long)
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Haochen Shi, Tianshi Zheng, Weiqi Wang, Baixuan Xu, Chunyang Li, Chunkit Chan, Tao Fan, Yangqiu Song
| Challenge: | Large Language Model (LLM) routing is a pivotal technique for navigating a diverse landscape of LLMs. |
| Approach: | They propose a flexible and scalable multi-dimensional routing framework that models the capability and knowledge of models. |
| Outcome: | The proposed framework can be used to generalize and identify top-performing models for group-level routing using modern benchmarks including MMLU-Pro, GPQA, BigGenBench, and LiveBench. |
Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction (2024.emnlp-main)
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| Challenge: | Existing approaches to condensing textual information into concise and structured tables are limited in their applicability in broader contexts. |
| Approach: | They propose a benchmark dataset for generating summary tables of competitions based on real-time commentary texts that incorporates large-scale textual information into concise and structured tables. |
| Outcome: | The proposed method exhibits strong generalization abilities, surpassing previous approaches on several other text-to-table datasets. |
Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset (2021.emnlp-main)
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| Challenge: | Existing evaluations on the population task are either not accurate (automatic evaluation with randomly sampled negative examples) or of small scale (human annotation). |
| Approach: | They propose a reasoning over commonsense knowledge bases (CSKBs) that are free-text and have a human annotation set to probe commonsensical reasoning. |
| Outcome: | The proposed model is based on a human-annotated evaluation set and is compared with existing models on the population task. |
COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective (2023.acl-long)
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Zhaowei Wang, Quyet V. Do, Hongming Zhang, Jiayao Zhang, Weiqi Wang, Tianqing Fang, Yangqiu Song, Ginny Wong, Simon See
| Challenge: | Existing efforts to detect commonsense causation from the causal inference perspective are inadequate to seize commonsensical causations. |
| Approach: | They propose a task to detect commonsense causation between two events in context . they propose 'contextualized commons sense causal reasoning' framework that uses covariates to remove confounding effects . |
| Outcome: | The proposed framework can detect commonsense causality more accurately than baselines. |
Patterns Over Principles: The Fragility of Inductive Reasoning in LLMs under Noisy Observations (2025.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated remarkable performance across various reasoning tasks. |
| Approach: | They propose a task that evaluates LLMs’ capability in inferring rules from data fused with noisy examples. |
| Outcome: | The proposed method outperforms other methods with minimal performance degradation under noise and counterfactual task gaps highlight LLMs’ reliance on memorized patterns over genuine abstraction. |
ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents (2025.emnlp-main)
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| Challenge: | Existing benchmarks focus on image-based question answering (QA) but ignore the fundamental challenges of efficient retrieval, comprehension, and reasoning within dense visual documents. |
| Approach: | They propose a novel multi-agent RAG framework tailored for complex reasoning across visual documents that employs a Gaussian Mixture Model (GMM)-based hybrid strategy to handle multi-modal retrieval. |
| Outcome: | The proposed framework outperforms existing methods by over 10% on the competitive ViDoSeek benchmark. |
ConKE: Conceptualization-Augmented Knowledge Editing in Large Language Models for Commonsense Reasoning (2025.findings-acl)
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| Challenge: | Existing knowledge editing methods face limited knowledge coverage in existing knowledge bases, infeasibility of annotating labels for an overabundance of commonsense knowledge, and strict knowledge formats. |
| Approach: | They propose a framework that integrates conceptualization and instantiation into the KE pipeline for LLMs to enhance their commonsense reasoning capabilities. |
| Outcome: | The proposed framework diagnoses implausible commonsense knowledge within an LLM and augments the source knowledge to be edited with conceptualization for stronger generalizability. |
EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association (2025.acl-long)
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Weiqi Wang, Limeng Cui, Xin Liu, Sreyashi Nag, Wenju Xu, Chen Luo, Sheikh Muhammad Sarwar, Yang Li, Hansu Gu, Hui Liu, Changlong Yu, Jiaxin Bai, Yifan Gao, Haiyang Zhang, Qi He, Shuiwang Ji, Yangqiu Song
| Challenge: | Goal-oriented script planning is used by humans to plan for typical activities . however, this capability remains underexplored due to several challenges . |
| Approach: | They propose a framework that enables product-enriched scripts by associating products with each step based on the semantic similarity between the actions and their purchase intentions. |
| Outcome: | The proposed framework can generate product-enriched scripts from 2.4 million scripts . human annotations are conducted to provide gold labels for a sampled subset . |
IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters (2026.acl-industry)
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Hongwei Zheng, Weiqi Wu, Zhengjia Wang, Guanyu Jiang, Haoming Li, Tianyu Wu, Yongchun Zhu, Jingwu Chen, Feng Zhang
| Challenge: | Existing efforts focus on activation within ongoing dialogues, while overlooking a key real-world bottleneck. |
| Approach: | They propose a conversation starter generation system that generates personalized starters to guide users into conversation without explicit user intent. |
| Outcome: | The proposed system improves user active days by +1.84 and click-through rate by +94.25 and has been deployed in production. |
CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers? (2025.findings-emnlp)
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Jiefu Ou, William Gantt Walden, Kate Sanders, Zhengping Jiang, Kaiser Sun, Jeffrey Cheng, William Jurayj, Miriam Wanner, Shaobo Liang, Candice Morgan, Seunghoon Han, Weiqi Wang, Chandler May, Hannah Recknor, Daniel Khashabi, Benjamin Van Durme
| Challenge: | CLAIMCHECK is an annotated dataset of NeurIPS 2023 and 2024 submissions and reviews from OpenReview. |
| Approach: | They annotate NeurIPS 2023 and 2024 submissions and reviews for weaknesses and dispute them for fine-grained labels of validity, objectivity, and type of the identified weaknesses. |
| Outcome: | The proposed dataset is richly annotated by ML experts for weaknesses statements in the reviews and the claims that they dispute, as well as fine-grained labels of validity, objectivity, and type of the identified weaknesses. |
MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding (2024.emnlp-main)
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Baixuan Xu, Weiqi Wang, Haochen Shi, Wenxuan Ding, Huihao Jing, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Long Chen, Yangqiu Song
| Challenge: | Existing methods for acquiring large-scale intentions generate product-centric intentions without product images and incur high costs for scalability. |
| Approach: | They propose a multimodal framework that allows Large Vision-Language Models to infer purchase intentions from multimodal product metadata and prioritize human-centric ones. |
| Outcome: | The proposed framework shows that it is robust to different prompts and superior to previous methods. |
Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection (2023.findings-emnlp)
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| Challenge: | Existing methods to construct CSKGs with large semantic coverage are expensive and introduce spurious noise. |
| Approach: | They propose a denoising framework that incorporates entity semantic information, global rules, and local structural information from the CSKG. |
| Outcome: | The proposed framework outperforms baseline methods in noise detection tasks on synthetic noisy CSKG benchmarks. |
From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery, evolving from task-specific automation tools into increasingly autonomous agents. |
| Approach: | They introduce a foundational three-level taxonomy to delineate their escalating autonomy and evolving responsibilities within the research lifecycle. |
| Outcome: | The proposed frameworks provide a conceptual architecture and strategic foresight to navigate and shape the future of AI-driven scientific discovery. |
Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models’ Uncertainty? (2025.acl-short)
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| Challenge: | Large language models (LLMs) are increasingly used in high-stakes domains, but their confidence is inconsistent in out-of-distribution scenarios. |
| Approach: | They define "marker confidence" as the observed accuracy when a model employs an epistemic marker. |
| Outcome: | The proposed model generalizes well within the same distribution, but its confidence is inconsistent in out-of-distribution scenarios. |
IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce (2024.findings-emnlp)
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Wenxuan Ding, Weiqi Wang, Sze Kwok, Minghao Liu, Tianqing Fang, Jiaxin Bai, Xin Liu, Changlong Yu, Zheng Li, Chen Luo, Qingyu Yin, Bing Yin, Junxian He, Yangqiu Song
| Challenge: | Existing approaches that distill intentions from LMs fail to generate meaningful and human-centric intentions applicable in real-world E-commerce contexts. |
| Approach: | They propose a double-task multiple-choice question answering benchmark to evaluate LMs' comprehension of purchase intentions in E-commerce. |
| Outcome: | The proposed benchmark consists of 4,360 carefully curated problems across three difficulty levels, constructed using an automated pipeline to ensure scalability on large E-commerce platforms. |
CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering (2023.findings-emnlp)
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| Challenge: | Existing approaches to zero-shot commonsense question answering use incomplete CSKBs . lack of human annotations makes sampled negative examples potentially uninformative and contradictory. |
| Approach: | They propose a framework that abstracts a commonsense knowledge triple to many higher-level instances, which increases the coverage of the CSKB and expands the ground-truth answer space. |
| Outcome: | Experiments show that CAR can generalize to zero-shot commonsense scenarios . lack of human annotations makes sampled negative examples potentially uninformative and contradictory. |
CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning (2024.acl-long)
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Weiqi Wang, Tianqing Fang, Chunyang Li, Haochen Shi, Wenxuan Ding, Baixuan Xu, Zhaowei Wang, Jiaxin Bai, Xin Liu, Cheng Jiayang, Chunkit Chan, Yangqiu Song
| Challenge: | Existing approaches to generalize commonsense reasoning lack instantiated knowledge and require pre-built concept taxonomies and annotations. |
| Approach: | They propose a framework that iteratively performs contextualized conceptualization and instantiation over commonsense knowledge bases by instructing large language models to generate both types of knowledge with critic filtering. |
| Outcome: | Empirical results show that distilling CANDLE on student models provides benefits across three downstream tasks. |
Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations (2024.findings-eacl)
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| Challenge: | Recent studies have demonstrated ChatGPT's remarkable few-shot, even zero-shot learning abilities when compared to other models. |
| Approach: | They quantitatively evaluate the performance of ChatGPT on inter-sentential relations such as temporal relations, causal relations, and discourse relations. |
| Outcome: | The proposed model performs well on temporal relations, causal relations, and discourse relations. |
GoldCoin: Grounding Large Language Models in Privacy Laws via Contextual Integrity Theory (2024.emnlp-main)
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| Challenge: | Existing research studies privacy by exploring various privacy attacks, defenses, and evaluations within narrowly predefined patterns. |
| Approach: | They propose a framework that leverages the theory of contextual integrity as a bridge to help LLMs understand the complex contexts for judicial assessing privacy violations. |
| Outcome: | The proposed framework bridges the theory of contextual integrity as a bridge, creating numerous synthetic scenarios grounded in relevant privacy statutes (e.g., HIPAA). |
Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)
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Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, Yangqiu Song
| Challenge: | Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions. |
| Approach: | They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions. |
| Outcome: | The proposed model outperforms state-of-the-art methods and shows its utility. |
SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding (2026.findings-acl)
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Yuqi Yang, Weiqi Wang, Baixuan Xu, Wei Fan, Qing Zong, Chunkit Chan, Zheye Deng, Xin Liu, Yifan Gao, Changlong Yu, Chen Luo, Yang Li, Zheng Li, Qingyu Yin, Bing Yin, Yangqiu Song
| Challenge: | Existing models fail to capture and model customer intention effectively because of insufficient information exploitation and only apparent information like descriptions and titles are used. |
| Approach: | They propose to exploit existing session data to capture and model intention in E-commerce product purchase sessions using a multimodal benchmark. |
| Outcome: | The proposed framework can bridge the gap between intention understanding in simplified research cases like co-buy intention and more complex yet practical scenarios like session history. |
FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery (2023.findings-acl)
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Changlong Yu, Weiqi Wang, Xin Liu, Jiaxin Bai, Yangqiu Song, Zheng Li, Yifan Gao, Tianyu Cao, Bing Yin
| Challenge: | Existing intention-based studies on recommendation tasks are limited and use models to implicitly model the intention memberships. |
| Approach: | They propose a framework that leverages the generation power of large language models and human-in-the-loop annotation to semi-automatically construct the intention knowledge graph. |
| Outcome: | The proposed framework can model e-commerce knowledge and have many potential applications. |
On-Policy Self-Distillation for Efficient Diffusion Language Models with Early-Stage Calibration (2026.findings-acl)
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Huaisheng Zhu, MingYu Liu, Junze Liu, Zhen Ge, Tian Wang, Jiri Gesi, Dakuo Wang, Weiqi Zhang, Houyu Zhang, Yufan Guo, Xian Li, Bing Yin, Sujay Sanghavi
| Challenge: | Recent studies have demonstrated that masked diffusion models (MDMs) can surpass autoregressive models (ARMs) in various tasks. |
| Approach: | They propose a method to calibrate early token predictions without demonstration data by distilling an unnormalized target distribution into the original model. |
| Outcome: | Experiments on math, planning, and RLHF tasks show that COPSD improves both effectiveness and efficiency, and further enhances performance when combined with supervised fine-tuning. |