Papers by Xun Zhang
How LLMs React to Industrial Spatio-Temporal Data? Assessing Hallucination with a Novel Traffic Incident Benchmark Dataset (2025.naacl-industry)
Copied to clipboard
Qiang Li, Mingkun Tan, Xun Zhao, Dan Zhang, Daoan Zhang, Shengzhao Lei, Anderson S. Chu, Lujun Li, Porawit Kamnoedboon
| Challenge: | Large language models (LLMs) have a high potential to digitize and enhance the health & public services industry. |
| Approach: | They propose to use a cross-lingual benchmark dataset to assess the robustness of state-of-the-art LLMs in the spatio vs temporal domain for traffic incident classification. |
| Outcome: | The proposed model performs well in the spatio-temporal domain and in the non-English context. |
Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning (2026.eacl-long)
Copied to clipboard
| Challenge: | Chain-of-Thought prompting is a powerful technique for enhancing language model’s reasoning capabilities, but generating long and correct CoT trajectories is challenging. |
| Approach: | They propose to align the steps of Chain-of-Thought reasoning with loop iterations and apply intermediate supervision during the training of Looped Transformers. |
| Outcome: | The proposed method generates accurate reasoning chains for complex problems exceeding training length, and improves performance of the auto-regressive model. |
Beyond Static Testbeds: An Interaction-Centric Agent Simulation Platform for Dynamic Recommender Systems (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing platforms lack a mechanism for user actions to dynamically reshape the environment. |
| Approach: | They propose a novel agent-based simulation platform for recommender systems with a robust interaction mechanism. |
| Outcome: | The proposed platform improves the credibility of the simulation and replicates the Matthew Effect and Brand Loyalty. |
MimicLM: Zero-Shot Voice Imitation through Autoregressive Modeling of Pseudo-Parallel Speech Corpora (2026.findings-acl)
Copied to clipboard
Tao Feng, Yuxiang Wang, Yuancheng Wang, Xueyao Zhang, Dekun Chen, Chaoren Wang, Xun Guan, Zhizheng Wu
| Challenge: | Existing approaches to voice imitation use complex model design and a quality ceiling when synthetic speech is used as training *sources*. |
| Approach: | They propose a model that uses synthetic speech as training *sources* while retaining real recordings as *targets*. |
| Outcome: | The proposed model outperforms existing methods in naturalness while maintaining competitive similarity scores across speaker identity, accent, and emotion dimensions. |
Enhancing Partially Relevant Video Retrieval with Robust Alignment Learning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods focus on enhancing multi-scale clip representations but lack robust data alignment . inherent data uncertainty renders PRVR vulnerable to distractor videos with spurious similarities . |
| Approach: | proposed framework for partially relevant video retrieval aims to retrieve untrimmed videos partially relevant to a given query. |
| Outcome: | The proposed framework can be seamlessly integrated into existing architectures. |
Datasets for Scientific Literature Understanding: A Survey (2026.findings-acl)
Copied to clipboard
| Challenge: | Empowering machines to understand scientific literature is crucial for accelerating scientific discovery and advancing the AI for Science paradigm. |
| Approach: | They propose a systematic taxonomy that organizes resources spanning structural understanding, text understanding, multimodal understanding and pre-training/instruction fine-tuning. |
| Outcome: | The proposed taxonomy organizes resources spanning structural understanding, text understanding, multimodal understanding and pre-training/instruction fine-tuning. |
Eureka: Neural Insight Learning for Knowledge Graph Reasoning (2022.coling-1)
Copied to clipboard
| Challenge: | Existing knowledge embedding methods have limited performance on knowledge graph reasoning tasks . eureka is empowered to learn seen relations with sufficient training triples . |
| Approach: | They propose a neural insight learning framework called Eureka to bridge the “seen” to “unsea” gap . Eureca is empowered to learn seen relations with sufficient training triples while providing flexibility to learn unseen relations given only one trigger . |
| Outcome: | The proposed framework outperforms state-of-the-art models on seen and unseen relations . it can learn seen and unseen relationships with sufficient training triples . |
GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing LLM leaderboards often reference scores reported in other papers without consistent settings and prompts, which may encourage cherry-picking favored settings and for better results. |
| Approach: | They propose an open-source and reproducible LLM evaluation suite built on top of OpenAI Evals that systematically evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories. |
| Outcome: | The evaluation suite is built on top of OpenAI Evals and evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories. |
Towards Generating Controllable and Solvable Geometry Problem by Leveraging Symbolic Deduction Engine (2025.acl-industry)
Copied to clipboard
Zhuoxuan Jiang, Tianyang Zhang, Peiyan Peng, Jing Chen, Yinong Xun, Haotian Zhang, Lichi Li, Yong Li, Shaohua Zhang
| Challenge: | Compared to math word problems, geometry problems emphasize multi-modal formats and the translation between informal and formal languages. |
| Approach: | They propose a symbolic deduction engine-based geometry problem generation framework that leverages a symbolic deduction engine to generate geometry problems. |
| Outcome: | The proposed method avoids inherent biases in translating natural language into formal language and guarantees to control the generated problems in terms of knowledge points and difficulties by an elaborate checking function. |
CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference (2025.acl-long)
Copied to clipboard
Jinglong Luo, Guanzhong Chen, Yehong Zhang, Shiyu Liu, Hui Wang, Yue Yu, Xun Zhou, Yuan Qi, Zenglin Xu
| Challenge: | Existing privacy-preserving Transformer Inference frameworks suffer from high computational overhead and performance losses. |
| Approach: | They propose a framework that integrates random permutations and SMPC to address the "impossible trinity" CENTAUR resists diverse data reconstruction attacks and boosts inference speed by 5.030.4 times . |
| Outcome: | CENTAUR achieves an unprecedented balance between privacy, efficiency, and performance. |
Towards Stable and Effective Reinforcement Learning for Mixture-of-Experts (2026.acl-long)
Copied to clipboard
| Challenge: | Reinforcement learning with verifiable rewards (RLVR) training with Mixture-of-Experts policies remains fragile and prone to reward collapse. |
| Approach: | They propose a router shift-based policy optimization method that computes a per-token router-shift ratio conditioned on the previously activated experts and applies stop-gradient and a lower-bound floor. |
| Outcome: | The proposed method achieves better performance and greater stability than previous methods. |
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing frameworks that share entity embeddings of knowledge graphs (KGs) would incur a severe privacy leakage. |
| Approach: | They propose a new attack method that aims to recover the original embedding information based on the known entity embeddables of FedE. |
| Outcome: | The proposed framework can be used to infer whether a specific relation exists in a private client. |
K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning (2025.naacl-long)
Copied to clipboard
| Challenge: | Strategic reasoning requires Large Language Model (LLM) agents to adapt their strategies dynamically in multi-agent environments. |
| Approach: | They propose a framework that enables Large Language Models to achieve varying levels of strategic depth by recursive mechanisms that allow agents to form higher order beliefs about others' beliefs. |
| Outcome: | The proposed framework enables LLMs to achieve varying levels of strategic depth, allowing agents to form higher order beliefs—beliefs about others’ beliefs. |
PibE-MPP: A Play-it-by-Ear Masking Performance Plug-in for LLMs (2026.findings-acl)
Copied to clipboard
Mengwei Wang, Simin Niu, Xun Liang, Yuefeng Ma, Sensen Zhang, Jiawei Yang, Shichao Song, Hanyu Wang, Huayi Lai
| Challenge: | Random masking is a widely adopted classic baseline in large language models (LLMs). |
| Approach: | They propose a play-it-by-ear masking performance plug-in which enables LLMs to adaptively select masking target combinations for each task. |
| Outcome: | The proposed performance plug-in retains the advantages and mitigates the drawbacks of random masking in large language models. |
Counterfactual Active Learning for Out-of-Distribution Generalization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies on active learning methods focus on the out-of-distribution generalization of out- of-distortion samples. |
| Approach: | They propose a counterfactual active learning approach that empowers active learning with counterfact thinking to bridge the seen samples with unseen cases. |
| Outcome: | The proposed approach outperforms existing active learning methods on public datasets with comparable IID performance. |
DiningBench: A Hierarchical Multi-view Benchmark for Perception and Reasoning in the Dietary Domain (2026.acl-long)
Copied to clipboard
| Challenge: | Existing vision-language models lack fine-grained classification, single-view imagery, and inaccurate metadata. |
| Approach: | They propose a hierarchical, multi-view benchmark to evaluate VLMs across three levels of cognitive complexity. |
| Outcome: | The proposed benchmark evaluates vision-language models across three levels of complexity . it systematically identifies five primary failure modes . the proposed benchmarks are available on https://github.com/meituan/DiningBench. |
SecCoder: Towards Generalizable and Robust Secure Code Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing secure code generation methods have limited generalizability to unseen test cases and poor robustness against the attacked model, leading to safety failures in code generation. |
| Approach: | They propose a generalizable and robust secure code generation method SecCoder by using in-context learning and the safe demonstration. |
| Outcome: | The proposed method achieves a significant security improvement of 7.20% on unseen test cases and better robustness against the attacked model. |
SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model (2025.acl-long)
Copied to clipboard
Xun Liang, Simin Niu, Zhiyu Li, Sensen Zhang, Hanyu Wang, Feiyu Xiong, Zhaoxin Fan, Bo Tang, Jihao Zhao, Jiawei Yang, Shichao Song, Mengwei Wang
| Challenge: | Existing approaches to integrating external knowledge into large language models (LLMs) however, the incorporation of external knowledge increases the vulnerability of LLMs . |
| Approach: | They propose a benchmark to evaluate the RAG security using a dataset . they classify attack tasks into silver noise, inter-context conflict, soft ad, and white Denial-of-Service . |
| Outcome: | The proposed benchmark evaluates the security of RAG against 14 representative RAG components. |
Navigating the OverKill in Large Language Models (2024.acl-long)
Copied to clipboard
Chenyu Shi, Xiao Wang, Qiming Ge, Songyang Gao, Xianjun Yang, Tao Gui, Qi Zhang, Xuanjing Huang, Xun Zhao, Dahua Lin
| Challenge: | Recent studies have highlighted a tendency among large language models to refuse to answer benign queries. |
| Approach: | They propose a model-agnostic approach to reduce excessive attention to harmful words like ‘kill’ and a method to decode the next-token predictions by contrastive decoding. |
| Outcome: | The proposed approach reduces the refusal rate by 20% while having little impact on safety. |
Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods that confuse tool utilization with knowledge reasoning harm readability and give rise to tool invocation hallucinations. |
| Approach: | They propose to decouple LLM from tool invocation tasks by establishing a memory module with explicit descriptions of query statements and a query memory module to facilitate the KGQA process. |
| Outcome: | The proposed method achieves state-of-the-art on WebQSP and CWQ benchmarks. |