Papers by Qing Zong
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)
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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song
| Challenge: | Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage. |
| Approach: | They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. |
| Outcome: | The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality. |
AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation (2024.acl-long)
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Zhaowei Wang, Wei Fan, Qing Zong, Hongming Zhang, Sehyun Choi, Tianqing Fang, Xin Liu, Yangqiu Song, Ginny Wong, Simon See
| 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. |
CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents (2026.acl-long)
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| Challenge: | Existing evaluations of Large Language Models (LLMs) focus on task completion, but neglect a crucial capability: the ability to devise and adjust cost-optimal plans in response to changing environments. |
| Approach: | They propose a scalable, cost-centric benchmark to evaluate agents’ economic reasoning and replanning abilities. |
| Outcome: | Evaluating leading open-sourced and proprietary models on CostBench reveals a substantial gap in cost-aware planning . |
ComparisonQA: Evaluating Factuality Robustness of LLMs Through Knowledge Frequency Control and Uncertainty (2025.findings-acl)
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| Challenge: | Existing studies on LLMs' factual knowledge are unreliable since the questions can vary not only in entity frequency but also in difficulty themselves. |
| Approach: | They propose a benchmark to study the role of knowledge frequency in the performance of large language models (LLMs) it aims to avoid possible semantic shortcuts which is a serious problem of current QA study. |
| Outcome: | The proposed method avoids possible semantic shortcuts and improves on existing proofs. |
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. |
DIXITWORLD: Evaluating Multimodal Abductive Reasoning in Vision-Language Models with Multi-Agent Dixit Gameplay (2026.acl-short)
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Yunxiang MO, Tianshi Zheng, Qing Zong, Jiayu Liu, Baixuan Xu, Yauwai Yim, Chunkit Chan, Jiaxin Bai, Yangqiu Song
| Challenge: | Existing evaluations of multimodal abductive reasoning are limited to static, single-agent tasks. |
| Approach: | They propose a multiagent evaluation suite that deconstructs the current evaluations of multimodal abductive reasoning in vision–language models. |
| Outcome: | The evaluation suite is based on two core components: DixitArena and DixitsBench. |
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. |
ContrastKV: Robust KV Cache Eviction via Contrastive Signal Fusion for Multi-Query Generalization (2026.acl-long)
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| Challenge: | Existing query-agnostic approaches rely on a single proxy query, leading to fragile eviction decisions under high evict ratios. |
| Approach: | They propose a query-agnostic KV cache eviction algorithm that exploits complementary semantic and non-semantic signals. |
| Outcome: | Experiments show that the proposed algorithm outperforms state-of-the-art methods while retaining up to 92% accuracy with only 20% of the KV cache budget. |