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Weiqi Wang

34 accepted papers

2026

NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents

Tianshi Zheng, Kelvin Kiu Wai Tam, Newt Nguyen Kim Hue Nam, Baixuan Xu, Zhaowei Wang, Cheng Jiayang +7
ICLR 2026poster

Large language models (LLMs) are emerging as powerful tools for scientific law discovery, a foundational challenge in AI-driven science. However, existing benchmarks for this task suffer from a fundamental methodological trilemma, forcing a trade-off between scientific relevance, scalability, and re…

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2026

Steering Beyond the Support: Adversarial Training on Unsupervised Jailbroken Activation Simulation

LUOYU CHEN, Weiqi Wang, Zhiyi Tian, Chenhan Zhang, Feng Wu, Jianhuan Huang +2
ICML 2026poster

Jailbreak prompts can trigger harmful comple- tions on aligned LLMs, In accordance, safety steering has been proposed: test-time activation interventions that steer jailbreak activations to trig- ger refusal while preserving benign utility. How- ever, existing steering methods are fundamentally supe…

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2025

A High-Precision Character Cartoon Style Transfer Method Based on VToonify and Diffusion Models

Weiting Wang, Weiqi Wang, Feilong Bao
ICASSP 2025accepted

In recent years, the technology for converting character images into cartoons has gained widespread attention on short video platforms, with diffusion models particularly standing out in the field of image style transfer. However, existing methods still have shortcomings in detail handling and chara…

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2025

CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers?

Jiefu Ou, William Walden, Kate Sanders, Zhengping Jiang, Kaiser Sun, Jeffrey Cheng +10
EMNLP 2025

A core part of scientific peer review involves providing expert critiques that directly assess the scientific claims a paper makes. While it is now possible to automatically generate plausible (if generic) reviews, ensuring that these reviews are sound and grounded in the papers’ claims remains chal

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2025

ConKE: Conceptualization-Augmented Knowledge Editing in Large Language Models for Commonsense Reasoning

Liyu Zhang, Weiqi Wang, Tianqing Fang, Yangqiu Song
ACL 2025finding

Knowledge Editing (KE) aims to adjust a Large Language Model’s (LLM) internal representations and parameters to correct inaccuracies and improve output consistency without incurring the computational expense of re-training the entire model. However, editing commonsense knowledge still faces difficul…

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2025

Controllable Style Arithmetic with Language Models

Weiqi Wang, Wengang Zhou, Zongmeng Zhang, Jie Zhao, Houqiang Li
ACL 2025long

Language models have shown remarkable capabilities in text generation, but precisely controlling their linguistic style remains challenging. Existing methods either lack fine-grained control, require extensive computation, or introduce significant latency. We propose Style Arithmetic (SA), a novel p…

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2025

EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association

Weiqi Wang, Limeng Cui, Xin Liu, Sreyashi Nag, Wenju Xu, Chen Luo +11
ACL 2025long

Goal-oriented script planning, or the ability to devise coherent sequences of actions toward specific goals, is commonly employed by humans to plan for typical activities. In e-commerce, customers increasingly seek LLM-based assistants to generate scripts and recommend products at each step, thereby…

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2025

From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery

Tianshi Zheng, Zheye Deng, Hong Ting Tsang, Weiqi Wang, Jiaxin Bai, Zihao Wang +1
EMNLP 2025

Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery, evolving from task-specific automation tools into increasingly autonomous agents and fundamentally redefining research processes and human-AI collaboration. This survey systematically charts this burgeoning field,

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2025

KnowShiftQA: How Robust are RAG Systems when Textbook Knowledge Shifts in K-12 Education?

Tianshi Zheng, Weihan Li, Jiaxin Bai, Weiqi Wang, Yangqiu Song
ACL 2025short

Retrieval-Augmented Generation (RAG) systems show remarkable potential as question answering tools in the K-12 Education domain, where knowledge is typically queried within the restricted scope of authoritative textbooks. However, discrepancies between these textbooks and the parametric knowledge in…

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2025

MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset

Weiqi Wang, Yangqiu Song
ACL 2025long

To enable Large Language Models (LLMs) to function as conscious agents with generalizable reasoning capabilities, it is crucial that they possess the ability to ***comprehend situational changes (transitions) in distribution*** triggered by environmental factors or actions from other agents. Despite…

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2025

On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions

Weiqi Wang, Tianqing Fang, Haochen Shi, Baixuan Xu, Wenxuan Ding, Liyu Zhang +5
EMNLP 2025

Conceptualization, a fundamental element of human cognition, plays a pivotal role in human generalizable reasoning.Generally speaking, it refers to the process of sequentially abstracting specific instances into higher-level concepts and then forming abstract knowledge that can be applied in unfamil

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2025

Patterns Over Principles: The Fragility of Inductive Reasoning in LLMs under Noisy Observations

Chunyang Li, Weiqi Wang, Tianshi Zheng, Yangqiu Song
ACL 2025finding

Inductive reasoning, a cornerstone of human cognition, enables generalization from limited data but hasn’t yet been fully achieved by large language models (LLMs). While modern LLMs excel at reasoning tasks, their ability to maintain stable and consistent rule abstraction under imperfect observation…

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2025

Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models’ Uncertainty?

Jiayu Liu, Qing Zong, Weiqi Wang, Yangqiu Song
ACL 2025short

As large language models (LLMs) are increasingly used in high-stakes domains, accurately assessing their confidence is crucial. Humans typically express confidence through epistemic markers (e.g., “fairly confident”) instead of numerical values. However, it remains unclear whether LLMs consistently…

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2025

Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering

Zixin Chen, Sicheng Song, KaShun Shum, Yanna Lin, Rui Sheng, Weiqi Wang +1
EMNLP 2025

Misleading visualizations, which manipulate chart representations to support specific claims, can distort perception and lead to incorrect conclusions. Despite decades of research, they remain a widespread issue, posing risks to public understanding and raising safety concerns for AI systems involve

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2024

AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph

Zhaowei Wang, Haochen Shi, Weiqi Wang, Tianqing Fang, Hongming Zhang, Sehyun Choi +2
NAACL 2024findings

Cognitive research indicates that abstraction ability is essential in human intelligence, which remains under-explored in language models. In this paper, we present AbsPyramid, a unified entailment graph of 221K textual descriptions of abstraction knowledge. While existing resources only touch nouns…

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2024

CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning

Weiqi Wang, Tianqing Fang, Chunyang Li, Haochen Shi, Wenxuan Ding, Baixuan Xu +6
ACL 2024long

The sequential process of conceptualization and instantiation is essential to generalizable commonsense reasoning as it allows the application of existing knowledge to unfamiliar scenarios. However, existing works tend to undervalue the step of instantiation and heavilyrely on pre-built concept taxo…

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2024

GoldCoin: Grounding Large Language Models in Privacy Laws via Contextual Integrity Theory

Wei Fan, Haoran Li, Zheye Deng, Weiqi Wang, Yangqiu Song
EMNLP 2024main

Privacy issues arise prominently during the inappropriate transmission of information between entities. Existing research primarily studies privacy by exploring various privacy attacks, defenses, and evaluations within narrowly predefined patterns, while neglecting that privacy is not an isolated, c…

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2024

IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce

Wenxuan Ding, Weiqi Wang, Sze Heng Douglas Kwok, Minghao Liu, Tianqing Fang, Jiaxin Bai +8
EMNLP 2024finding

Enhancing Language Models’ (LMs) ability to understand purchase intentions in E-commerce scenarios is crucial for their effective assistance in various downstream tasks. However, previous approaches that distill intentions from LMs often fail to generate meaningful and human-centric intentions appli…

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2024

MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding

Baixuan Xu, Weiqi Wang, Haochen Shi, Wenxuan Ding, Huihao Jing, Tianqing Fang +9
EMNLP 2024main

Improving user experience and providing personalized search results in E-commerce platforms heavily rely on understanding purchase intention. However, existing methods for acquiring large-scale intentions bank on distilling large language models with human annotation for verification. Such an approa…

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2024

NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surrounding

Chunkit Chan, Cheng Jiayang, Yauwai Yim, Zheye Deng, Wei Fan, Haoran Li +4
EMNLP 2024finding

Large Language Models (LLMs) have sparked substantial interest and debate concerning their potential emergence of Theory of Mind (ToM) ability. Theory of mind evaluations currently focuses on testing models using machine-generated data or game settings prone to shortcuts and spurious correlations, w…

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2024

Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction

Zheye Deng, Chunkit Chan, Weiqi Wang, Yuxi Sun, Wei Fan, Tianshi Zheng +2
EMNLP 2024main

The task of condensing large chunks of textual information into concise and structured tables has gained attention recently due to the emergence of Large Language Models (LLMs) and their potential benefit for downstream tasks, such as text summarization and text mining. Previous approaches often gen…

Cited by 9Source ↗PDF ↗code ↗Scholar ↗
2023

CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering

Weiqi Wang, Tianqing Fang, Wenxuan Ding, Baixuan Xu, Xin Liu, Yangqiu Song +1
EMNLP 2023long findings

The task of zero-shot commonsense question answering evaluates models on their capacity to reason about general scenarios beyond those presented in specific datasets. Existing approaches for tackling this task leverage external knowledge from CommonSense Knowledge Bases (CSKBs) by pre-training the m…

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2023

CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning

Weiqi Wang, Tianqing Fang, Baixuan Xu, Chun Yi Louis Bo, Yangqiu Song, Lei Chen
ACL 2023long

Commonsense reasoning, aiming at endowing machines with a human-like ability to make situational presumptions, is extremely challenging to generalize. For someone who barely knows about “meditation,” while is knowledgeable about “singing,” he can still infer that “meditation makes people relaxed” fr…

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2023

COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective

Zhaowei Wang, Quyet V. Do, Hongming Zhang, Jiayao Zhang, Weiqi Wang, Tianqing Fang +3
ACL 2023long

Detecting commonsense causal relations (causation) between events has long been an essential yet challenging task. Given that events are complicated, an event may have different causes under various contexts. Thus, exploiting context plays an essential role in detecting causal relations. Meanwhile,…

Cited by 17Source ↗PDF ↗code ↗Scholar ↗
2023

Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints

Jiaxin Bai, Xin Liu, Weiqi Wang, Chen Luo, Yangqiu Song
NeurIPS 2023poster

Querying knowledge graphs (KGs) using deep learning approaches can naturally leverage the reasoning and generalization ability to learn to infer better answers. Traditional neural complex query answering (CQA) approaches mostly work on entity-centric KGs. However, in the real world, we also need to…

Cited by 23Source ↗PDF ↗code ↗Scholar ↗
2023

FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery

Changlong Yu, Weiqi Wang, Xin Liu, Jiaxin Bai, Yangqiu Song, Zheng Li +3
ACL 2023findings

Understanding users’ intentions in e-commerce platforms requires commonsense knowledge. In this paper, we present FolkScope, an intention knowledge graph construction framework, to reveal the structure of humans’ minds about purchasing items. As commonsense knowledge is usually ineffable and not exp…

Cited by 21Source ↗PDF ↗code ↗Scholar ↗
2023

Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection

Zheye Deng, Weiqi Wang, Zhaowei Wang, Xin Liu, Yangqiu Song
EMNLP 2023long findings

Commonsense Knowledge Graphs (CSKGs) are crucial for commonsense reasoning, yet constructing them through human annotations can be costly. As a result, various automatic methods have been proposed to construct CSKG with larger semantic coverage. However, these unsupervised approaches introduce spuri…

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2023

QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering

Haochen Shi, Weiqi Wang, Tianqing Fang, Baixuan Xu, Wenxuan Ding, Xin Liu +1
EMNLP 2023short findings

Zero-shot commonsense Question-Answering (QA) requires models to reason about general situations beyond specific benchmarks. State-of-the-art approaches fine-tune language models on QA pairs constructed from CommonSense Knowledge Bases (CSKBs) to equip the models with more commonsense knowledge in a…

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2023

Rearrange Indoor Scenes for Human-Robot Co-Activity

Weiqi Wang, Zihang Zhao, Ziyuan Jiao, Yixin Zhu, Song-Chun Zhu, Hangxin Liu
ICRA 2023poster

We present an optimization-based framework for rearranging indoor furniture to accommodate human-robot co-activities better. The rearrangement aims to afford sufficient accessible space for robot activities without compromising everyday human activities. To retain human activities, our algorithm pre…

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2023

StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding

Cheng Jiayang, Lin Qiu, Tsz Ho CHAN, Tianqing Fang, Weiqi Wang, Chunkit Chan +6
EMNLP 2023long main

Analogy-making between narratives is crucial for human reasoning. In this paper, we evaluate the ability to identify and generate analogies by constructing a first-of-its-kind large-scale story-level analogy corpus, \textsc{StoryAnalogy}, which contains 24K story pairs from diverse domains with huma…

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2021

Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset

Tianqing Fang, Weiqi Wang, Sehyun Choi, Shibo Hao, Hongming Zhang, Yangqiu Song +1
EMNLP 2021main

Reasoning over commonsense knowledge bases (CSKB) whose elements are in the form of free-text is an important yet hard task in NLP. While CSKB completion only fills the missing links within the domain of the CSKB, CSKB population is alternatively proposed with the goal of reasoning unseen assertions…

Cited by 23Source ↗PDF ↗code ↗Scholar ↗
2021

Efficient Task Planning for Mobile Manipulation: a Virtual Kinematic Chain Perspective

Ziyuan Jiao, Zeyu Zhang, Weiqi Wang, David Han, Song-Chun Zhu, Yixin Zhu +1
IROS 2021poster

We present a Virtual Kinematic Chain (VKC) perspective, a simple yet effective method, to improve task planning efficacy for mobile manipulation. By consolidating the kinematics of the mobile base, the arm, and the object being manipulated collectively as a whole, this novel VKC perspective naturall…

Cited by 21Source ↗code ↗Scholar ↗
2021

SEQ-CPC : Sequential Contrastive Predictive Coding for Automatic Speech Recognition

Yulong Chen, Jianping Zhao, Weiqi Wang, Ming Fang, Haimei Kang, Lu Wang +4
ICASSP 2021accepted

Inspired by the contrastive predictive coding (CPC), we propose a feature representation scheme for automatic speech recognition (ASR), which encodes sequential dependency information from raw audio signals. Following the original CPC, for a given frame, mutual information (MI) lower bound is maximi…

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