Papers by Tianqing Fang

ConstraintChecker: A Plugin for Large Language Models to Reason on Commonsense Knowledge Bases (2024.eacl-long)

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.

Copied to clipboard

Quyet V. Do, Tianqing Fang, Shizhe Diao, Zhaowei Wang, Yangqiu Song

Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs (2024.acl-long)

Challenge:	Reasoning over Commonsense Knowledge Bases (CSKBs) is a way to acquire new commonsense knowledge based on reference knowledge in original CSKB and external prior knowledge.
Approach:	They propose a plugin to provide and check explicit relational constraints over prompting techniques.
Outcome:	The proposed method improves on existing prompting techniques and CSKB reasoning.

Copied to clipboard

Tianqing Fang, Zeming Chen, Yangqiu Song, Antoine Bosselut

Recall with Reasoning: Chain-of-Thought Distillation for Mamba’s Long-Context Memory and Extrapolation (2025.emnlp-main)

Challenge:	Currently, language models struggle to generate commonsense inferences for complex tasks due to data scarcity and the difficulty of reasoning over multiple pieces of information.
Approach:	They propose a dataset to generate commonsense inferences from commonsensible data . they use a commonsence knowledge graph to extract and form questions from existing commonseense knowledge graphs.
Outcome:	The proposed dataset improves the ability of language models to reason about complex events without expensive human annotations.

Copied to clipboard

Jun-Yu Ma, Tianqing Fang, Zhisong Zhang, Hongming Zhang, Haitao Mi, Dong Yu

Text Generation

Challenge:	Existing long-context memory methods such as Mamba struggle with long-constituency when the length of the processed text exceeds the model's training length.
Approach:	They propose a method that uses chain-of-thought summarization to teach Mamba to actively recall and reason over long contexts.
Outcome:	Experiments on LONGMEMEVAL and HELMET show that RwR outperforms existing long-term memory methods while preserving short-context capabilities.

StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding (2023.emnlp-main)

Copied to clipboard

Cheng Jiayang, Lin Qiu, Tsz Chan, Tianqing Fang, Weiqi Wang, Chunkit Chan, Dongyu Ru, Qipeng Guo, Hongming Zhang, Yangqiu Song, Yue Zhang, Zheng Zhang

On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions (2025.findings-emnlp)

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.

Copied to clipboard

Weiqi Wang, Tianqing Fang, Haochen Shi, Baixuan Xu, Wenxuan Ding, Liyu Zhang, Wei Fan, Jiaxin Bai, Haoran Li, Xin Liu, Yangqiu Song

WebRollback: Enhancing Web Agents with Explicit Rollback Mechanisms (2026.eacl-short)

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 .

Copied to clipboard

Zhisong Zhang, Tianqing Fang, Kaixin Ma, Wenhao Yu, Hongming Zhang, Haitao Mi, Dong Yu

Challenge:	Recent studies have adopted a greedy one-way search strategy to deal with dynamic web environments.
Approach:	They propose to integrate a rollback mechanism into web agents to allow them to revert back to a previous state in navigation trajectory.
Outcome:	The proposed method is able to revert back to a previous state in its navigation trajectory, allowing the models to directly control the search process.

OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization (2025.acl-long)

Copied to clipboard

Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Hongming Zhang, Tianqing Fang, Zhenzhong Lan, Dong Yu

Multimodality

Challenge:	Existing studies focus on building text-only agents in synthetic environments where the reward signals are clearly defined.
Approach:	They propose a multimodal web agent that can autonomously conduct real-world exploration and improve itself after each iteration.
Outcome:	The proposed agent improves itself after each iteration, demonstrating strong performance across multiple test sets.

Weakly Supervised Text Classification using Supervision Signals from a Language Model (2022.findings-naacl)

Copied to clipboard

Ziqian Zeng, Weimin Ni, Tianqing Fang, Xiang Li, Xinran Zhao, Yangqiu Song

Low-Resource NLP

Challenge:	Existing weakly supervised text classification methods require a large number of annotated data and human annotations are expensive.
Approach:	They propose to query a masked language model with cloze style prompts to obtain supervision signals.
Outcome:	The proposed method outperforms baseline methods on three datasets by 2%, 4%, and 3%.

QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for Zero-Shot Commonsense Question Answering (2023.findings-emnlp)

Copied to clipboard

Haochen Shi, Weiqi Wang, Tianqing Fang, Baixuan Xu, Wenxuan Ding, Xin Liu, Yangqiu Song

KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection (2023.emnlp-main)

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.

Copied to clipboard

Sehyun Choi, Tianqing Fang, Zhaowei Wang, Yangqiu Song

Information Retrieval

Challenge:	Existing studies indicate that language models generate non-factual information that is not supported by evidence with a high level of confidence.
Approach:	They propose a knowledge-constrained decoding method that guides a frozen LLM to generate text aligned with the reference knowledge at each decoding step.
Outcome:	The proposed method reduces the risk of misinformation generated by LLMs by reducing training costs and catastrophic forgetting for multi-tasking models.

AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph (2024.findings-naacl)

Copied to clipboard

Zhaowei Wang, Haochen Shi, Weiqi Wang, Tianqing Fang, Hongming Zhang, Sehyun Choi, Xin Liu, Yangqiu Song

WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models (2026.acl-long)

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.

Copied to clipboard

Rui Wang, Ce Zhang, Jun-Yu Ma, Jianshu Zhang, Hongru Wang, Yi Chen, Boyang Xue, Tianqing Fang, Zhisong Zhang, Hongming Zhang, Haitao Mi, Dong Yu, Kam-Fai Wong

Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset (2021.emnlp-main)

Challenge:	Existing agentic systems are retrieval-heavy but reasoning-light . current systems lack compositional reasoning, a key component of deep research .
Approach:	They propose a data synthesis pipeline WebAggregator to shift agentic paradigm . they use Proactive Explorer to collect interconnected knowledge and Compositional Logic Proposer to weave knowledge into complex questions .
Outcome:	The proposed pipeline surpasses GPT-4.1 and matches Claude-3.7-Sonnet on GAIA, WebWalkerQA, and XBench.

Copied to clipboard

Tianqing Fang, Weiqi Wang, Sehyun Choi, Shibo Hao, Hongming Zhang, Yangqiu Song, Bin He

Probing Toxic Content in Large Pre-Trained Language Models (2021.acl-long)

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.

Copied to clipboard

Nedjma Ousidhoum, Xinran Zhao, Tianqing Fang, Yangqiu Song, Dit-Yan Yeung

DivScene: Towards Open-Vocabulary Object Navigation with Large Vision Language Models in Diverse Scenes (2025.findings-emnlp)

Challenge:	Existing studies on pre-trained language models have shown that they carry harmful biases towards different social groups.
Approach:	They propose a method to probe English, French, and Arabic PTLMs and quantify the potentially harmful content they convey with respect to a set of templates.
Outcome:	The proposed method analyzes PTLMs to predict masked tokens at the end of sentences to assess their toxicity.

Copied to clipboard

Zhaowei Wang, Hongming Zhang, Tianqing Fang, Ye Tian, Yue Yang, Kaixin Ma, Xiaoman Pan, Yangqiu Song, Dong Yu

Multimodality

Challenge:	Large Vision-Language Models (LVLMs) have achieved significant progress in tasks like visual question answering and document understanding.
Approach:	They introduce DivScene, a large-scale dataset with 4,614 houses across 81 scene types and 5,707 kinds of target objects.
Outcome:	The proposed dataset provides a much greater diversity of target objects and scene types than existing datasets, enabling a comprehensive task evaluation.

COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective (2023.acl-long)

Copied to clipboard

Zhaowei Wang, Quyet V. Do, Hongming Zhang, Jiayao Zhang, Weiqi Wang, Tianqing Fang, Yangqiu Song, Ginny Wong, Simon See

Commonsense Reasoning

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.

SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller (2022.emnlp-main)

Copied to clipboard

Zhaowei Wang, Hongming Zhang, Tianqing Fang, Yangqiu Song, Ginny Wong, Simon See

Text Generation

Challenge:	In this paper, we propose a new task of sub-event generation for an unseen process . we use a framework to generate coherent sub-Event sequences for unseened processes .
Approach:	They propose a task of sub-event generation for an unseen process to evaluate the understanding of the coherence of subevent actions and objects.
Outcome:	The proposed framework can generate coherent sub-event sequences for unseen processes . it can also decode more coherent subevents, demonstrating its effectiveness .

WebCoT: Enhancing Web Agent Reasoning by Reconstructing Chain-of-Thought in Reflection, Branching, and Rollback (2025.findings-emnlp)

Copied to clipboard

Minda Hu, Tianqing Fang, Jianshu Zhang, Jun-Yu Ma, Zhisong Zhang, Jingyan Zhou, Hongming Zhang, Haitao Mi, Dong Yu, Irwin King

AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation (2024.acl-long)

Challenge:	Web agents powered by Large Language Models lack the ability to perform in uncertain web environments.
Approach:	They propose to reconstruct web agents' reasoning skills into chain-of-thought rationales by fine-tuning their LLM backbone into a web-based model.
Outcome:	The proposed approach significantly improves the agent self-improving benchmark OpenWebVoyager, demonstrating that it can be used to improve the agent's reasoning skills.

Copied to clipboard

Zhaowei Wang, Wei Fan, Qing Zong, Hongming Zhang, Sehyun Choi, Tianqing Fang, Xin Liu, Yangqiu Song, Ginny Wong, Simon See

Explainability & Interpretability in NLP

Challenge:	Existing work shows that LLMs are deficient in abstract ability, and how to improve it remains unexplored.
Approach:	They propose a framework AbsInstruct to enhance LLMs’ abstract ability through instruction tuning.
Outcome:	The proposed framework can enhance LLMs’ abstraction ability with strong generalization performance while maintaining their general instruction-following abilities.

WebEvolver: Enhancing Web Agent Self-Improvement with Co-evolving World Model (2025.emnlp-main)

Copied to clipboard

Tianqing Fang, Hongming Zhang, Zhisong Zhang, Kaixin Ma, Wenhao Yu, Haitao Mi, Dong Yu

Challenge:	Agent self-improvement, where agents train their underlying Large Language Model (LLM) on self-sampled trajectories, shows promising results but often stagnates in web environments due to limited exploration and under-utilization of pretrained web knowledge.
Approach:	They propose a co-evolving Large Language Model (LLM) that predicts the next observation based on current observation and action within the web environment.
Outcome:	The proposed framework shows that agents can perform better in real-world web environments without using any distillation from more powerful close-sourced models.

MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation (2022.findings-emnlp)

Copied to clipboard

Ying Su, Zihao Wang, Tianqing Fang, Hongming Zhang, Yangqiu Song, Tong Zhang

Attention Entropy is a Key Factor: An Analysis of Parallel Context Encoding with Full-attention-based Pre-trained Language Models (2025.acl-long)

Challenge:	Existing approaches to commonsense reasoning include fine-tuning large pre-trained language models or injecting the entire knowledge base for CKGC.
Approach:	They propose to learn commonsense knowledge representation by using a multi-alternative contrastive learning framework on COmmonsense Knowledge graphs.
Outcome:	Extensive experiments show that the proposed framework is effective in commonsense reasoning tasks.

Copied to clipboard

Zhisong Zhang, Yan Wang, Xinting Huang, Tianqing Fang, Hongming Zhang, Chenlong Deng, Shuaiyi Li, Dong Yu

ConKE: Conceptualization-Augmented Knowledge Editing in Large Language Models for Commonsense Reasoning (2025.findings-acl)

Challenge:	Large language models have demonstrated remarkable performance across a wide range of language tasks due to their remarkable ability in context modeling.
Approach:	They propose to use parallel context encoding to reduce attention entropy by incorporating attention sinks and selective mechanisms to reduce irregular attention . they also propose to incorporate attention sink mechanisms into the parallel encoded context to reduce the irregular attention.
Outcome:	The proposed methods lower irregular attention entropy and narrow performance gaps.

Copied to clipboard

Liyu Zhang, Weiqi Wang, Tianqing Fang, Yangqiu Song

Measure Twice, Click Once: Co-evolving Proposer and Visual Critic via Reinforcement Learning for GUI Grounding (2026.acl-long)

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.

Copied to clipboard

Wenkai Wang, Xiyun Li, Hongcan Guo, Wenhao Yu, Tianqing Fang, Haitao Mi, Dong Yu, Shengyu Zhang

Multimodality

Challenge:	Graphical User Interface (GUI) grounding requires mapping natural language instructions to precise pixel coordinates due to visually homogeneous elements and dense layouts.
Approach:	They propose to replace static consistency strategies with a learnable selection mechanism that selects the optimal target by critiquing its own proposals rendered on the screenshot.
Outcome:	The proposed model significantly improves both grounding and critiquing capabilities over 6 benchmarks.

Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning (2024.findings-naacl)

Copied to clipboard

Tianqing Fang, Zhaowei Wang, Wenxuan Zhou, Hongming Zhang, Yangqiu Song, Muhao Chen

Doolittle: Benchmarks and Corpora for Academic Writing Formalization (2023.emnlp-main)

Challenge:	Event temporal reasoning aims at identifying the temporal relations between two or more events from narratives.
Approach:	They propose to detect knowledge conflicts in event temporal reasoning using bias indicators such as event relation prior bias, tense bias, narrative bias, and dependency bias.
Outcome:	The proposed method can be applied to Pre-trained Language Models and Large Language Model (LLMs) as additional training data or demonstrations for In- Context Learning.

Copied to clipboard

Shizhe Diao, Yongyu Lei, Liangming Pan, Tianqing Fang, Wangchunshu Zhou, Sedrick Keh, Min-Yen Kan, Tong Zhang

MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding (2024.emnlp-main)

Challenge:	Existing methods of language refinement focus on narrow, specific linguistic features within isolated sentences, such as grammatical errors and improper word use.
Approach:	They propose a task to improve the overall quality of academic writing at paragraph level by integrating automatic feedback into the training process.
Outcome:	The proposed task improves the overall quality of formal academic writing at the paragraph level.

Copied to clipboard

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

Green & Sustainable NLP

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.

Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification (2026.findings-acl)

Copied to clipboard

Yuxuan Wan, Tianqing Fang, Zaitang LI, Yintong Huo, Wenxuan Wang, Haitao Mi, Dong Yu, Michael R. Lyu

Challenge:	Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving.
Approach:	They propose an inference-time scaling of verification wherein an agent self-improves at test time by evaluating its generated answers.
Outcome:	The proposed model outperforms vanilla agent-as-judge and LLM judge baselines by 12%–48% in meta-evaluation F1 score.

IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce (2024.findings-emnlp)

Copied to clipboard

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

CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering (2023.findings-emnlp)

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.

Copied to clipboard

Weiqi Wang, Tianqing Fang, Wenxuan Ding, Baixuan Xu, Xin Liu, Yangqiu Song, Antoine Bosselut

CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning (2024.acl-long)

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.

Copied to clipboard

Weiqi Wang, Tianqing Fang, Chunyang Li, Haochen Shi, Wenxuan Ding, Baixuan Xu, Zhaowei Wang, Jiaxin Bai, Xin Liu, Cheng Jiayang, Chunkit Chan, Yangqiu Song

Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations (2024.findings-eacl)

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.

Copied to clipboard

Chunkit Chan, Cheng Jiayang, Weiqi Wang, Yuxin Jiang, Tianqing Fang, Xin Liu, Yangqiu Song

On-the-fly Denoising for Data Augmentation in Natural Language Understanding (2024.findings-eacl)

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.

Copied to clipboard

Tianqing Fang, Wenxuan Zhou, Fangyu Liu, Hongming Zhang, Yangqiu Song, Muhao Chen

Low-Resource NLP

Challenge:	Existing methods to improve data augmentation performance may introduce noisy data that impairs training.
Approach:	They propose an on-the-fly denoising technique that learns from soft augmented labels provided by an organic teacher model trained on the cleaner original dataset.
Outcome:	The proposed method improves on text classification and question-answering tasks on general augmentation techniques and prevents overfitting on noisy labels.

Concept-Reversed Winograd Schema Challenge: Evaluating and Improving Robust Reasoning in Large Language Models via Abstraction (2025.naacl-short)

Copied to clipboard

Kaiqiao Han, Tianqing Fang, Zhaowei Wang, Yangqiu Song, Mark Steedman

Robustness in NLP

Challenge:	Recent research has revealed that Large Language Models (LLMs) often experience issues with hallucinations and unreliable reasoning due to semantic associations and superficial logical chains.
Approach:	They propose a concept-reversed Winograd Schema Challenge dataset to evaluate the robustness of Large Language Models (LLMs) they propose Abstraction-of-Thought (AoT) method for recovering adversarial cases to normal cases using conceptual abstraction to improve LLMs’ robustness and consistency in reasoning.
Outcome:	The proposed method improves LLMs’ robustness and consistency in reasoning under adversarial and long-tail scenarios.

PseudoReasoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population (2022.findings-emnlp)

Copied to clipboard

Tianqing Fang, Quyet V. Do, Hongming Zhang, Yangqiu Song, Ginny Y. Wong, Simon See