Papers by Kexin Wang
LLM-VA: Resolving the Jailbreak-Overrefusal Trade-off via Vector Alignment (2026.acl-long)
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| Challenge: | Existing vector steering methods adjust the magnitude of answer vectors, but this creates a fundamental trade-off—reducing jailbreak increases over-refusal. |
| Approach: | They propose a method which aligns va with vb through closed-form weight updates, making the model’s willingness to respond causally dependent on its safety assessment. |
| Outcome: | Experiments on 12 LLMs show that the proposed method achieves 11.45% higher F1 than the best baseline while preserving 95.92% utility. |
Flames: Benchmarking Value Alignment of LLMs in Chinese (2024.naacl-long)
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Kexin Huang, Xiangyang Liu, Qianyu Guo, Tianxiang Sun, Jiawei Sun, Yaru Wang, Zeyang Zhou, Yixu Wang, Yan Teng, Xipeng Qiu, Yingchun Wang, Dahua Lin
| Challenge: | Existing benchmarks for large language models (LLMs) do not accurately uncover safety vulnerabilities in LLMs. |
| Approach: | They propose a value alignment benchmark called Flames that encompasses both harmlessness principles and a unique morality dimension that integrates specific Chinese values such as harmony. |
| Outcome: | The proposed model performs poorly on Flames, particularly in safety and fairness dimensions. |
MOSPC: MOS Prediction Based on Pairwise Comparison (2023.acl-short)
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| Challenge: | et al., 2016a) show that MOS prediction model can improve ranking accuracy of speech quality. |
| Approach: | They propose a general framework for MOS prediction based on pair comparison . they use C-Mixup algorithm to enhance generalization performance of MOSPC . |
| Outcome: | The proposed model outperforms baselines on most correlation coefficient metrics . it also surpasses the strong baseline in ranking accuracy on each fine-grained segment. |
LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)
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Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang
| Challenge: | Currently, most research focuses on the bidding algorithms used within auction mechanisms. |
| Approach: | They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process. |
| Outcome: | The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process. |
Sticking to the Mean: Detecting Sticky Tokens in Text Embedding Models (2025.acl-long)
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| Challenge: | Sticky tokens, when repeatedly inserted into sentences, pull sentence similarity toward a certain value, disrupting the normal distribution of embedding distances and degrading downstream performance. |
| Approach: | They propose a method to detect “sticky tokens” by sentence and token filtering and apply it to 40 checkpoints across 14 model families. |
| Outcome: | The proposed method detects 868 sticky tokens across 14 models and shows that their presence does not correlate with model size or vocabulary size. |
Parameter-Efficient Transfer Learning for End-to-end Speech Translation (2024.lrec-main)
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| Challenge: | Existing approaches to improve end-to-end speech translation are limited by the availability of labeled data. |
| Approach: | They propose a method which utilizes two lightweight adaptation techniques to modulate Attention and the Feed-Forward Network while preserving the capabilities of pre-trained models. |
| Outcome: | The proposed method outperforms baseline models and significantly improves performance in low-resource settings. |
Fake Alignment: Are LLMs Really Aligned Well? (2024.naacl-long)
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Yixu Wang, Yan Teng, Kexin Huang, Chengqi Lyu, Songyang Zhang, Wenwei Zhang, Xingjun Ma, Yu-Gang Jiang, Yu Qiao, Yingchun Wang
| Challenge: | Existing studies on large language models have shown that they are poorly aligned in practice. |
| Approach: | They propose a framework to evaluate safety in large language models . they propose two new metrics to quantify fake alignment and obtain corrected performance estimation. |
| Outcome: | The proposed framework and two metrics show that some models with purported safety are poorly aligned in practice. |
Semantic Similarity Models for Depression Severity Estimation (2023.emnlp-main)
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| Challenge: | Public health systems have limited capacity for case detection and diagnosis due to the widespread use of social media. |
| Approach: | They propose to use social media content to generate semantic rankings for depressive symptoms and severity levels and use them to predict symptoms severity. |
| Outcome: | The proposed pipeline improves on two Reddit-based benchmarks and shows that it is more efficient than state-of-the-art in terms of measuring depression level. |
UKP-SQuARE v2: Explainability and Adversarial Attacks for Trustworthy QA (2022.aacl-demo)
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Rachneet Sachdeva, Haritz Puerto, Tim Baumgärtner, Sewin Tariverdian, Hao Zhang, Kexin Wang, Hossain Shaikh Saadi, Leonardo F. R. Ribeiro, Iryna Gurevych
| Challenge: | Question Answering (QA) systems rely on deep neural networks, which are difficult to interpret by humans. |
| Approach: | They propose an interpretable model that provides an explanation infrastructure for comparing models based on saliency maps and graph-based explanations. |
| Outcome: | The proposed methods can be used to compare models based on saliency maps and graph-based explanations. |
ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Drafting (2025.acl-long)
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Steven H Wang, Maksim Zubkov, Kexin Fan, Sarah Harrell, Yuyang Sun, Wei Chen, Andreas Plesner, Roger Wattenhofer
| Challenge: | Contract clause retrieval is critical to contract drafting because of its high quality and complexity. |
| Approach: | They propose the first expert-annotated benchmark specifically designed for contract clause retrieval . ACORD focuses on complex contract clauses such as Limitation of Liability, Indemnification, Change of Control . |
| Outcome: | The atticus clause retrieval dataset shows promising results but needs improvement . the benchmark can be used as an IR benchmark for the NLP community . |
MMDEND: Dendrite-Inspired Multi-Branch Multi-Compartment Parallel Spiking Neuron for Sequence Modeling (2025.acl-long)
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Kexin Wang, Yuhong Chou, Di Shang, Shijie Mei, Jiahong Zhang, Yanbin Huang, Man Yao, Bo Xu, Guoqi Li
| Challenge: | Vanilla spiking neurons are simplified from complex biological neurons with dendrites, soma, and synapses into single somatic compartments. |
| Approach: | They propose a multi-branch, multi-compartment parallel spiking dendritic neuron with a proportion-adjustable multi-branched structure that enables long-term temporal dependencies. |
| Outcome: | The proposed model achieves better long-sequence modeling capability with fewer parameters and lower energy consumption. |
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning (2021.findings-emnlp)
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| Challenge: | Existing methods to learn sentence embeddings require labeled data, but it is expensive. |
| Approach: | They propose an unsupervised method which learns sentence embeddings using unlabeled data . they propose a transformer-based sequence denoising auto-encoder which can be used for training . |
| Outcome: | The proposed method outperforms existing methods on four datasets from heterogeneous domains. |
PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)
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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei
| Challenge: | Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. |
| Approach: | They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios. |
| Outcome: | The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics. |
Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking (2025.emnlp-main)
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| Challenge: | Existing methods to watermark low-entropy content are expensive and risky . IE reduces parameter size by 99% while achieving performance on par with state-of-the-art methods . |
| Approach: | They propose a logit-based watermarking paradigm that uses entropy-based features to predict whether the next token is high or low. |
| Outcome: | The proposed method reduces parameter size by 99% while achieving performance on par with state-of-the-art methods. |
LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models (2026.acl-long)
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Ming Zhang, Yujiong Shen, Jingyi Deng, Yuhui Wang, Huayu Sha, Kexin Tan, Qiyuan Peng, Yue Zhang, Junzhe Wang, Shichun Liu, Yueyuan Huang, Jingqi Tong, Changhao Jiang, Yilong Wu, Zhihao Zhang, Mingqi Wu, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing evaluation of Large Language Models on static benchmarks is vulnerable to data contamination and leaderboard overfitting. |
| Approach: | LLMEval-Fair framework provides a framework for dynamic evaluation of Large Language Models . evaluators use a proprietary bank of 220k graduate-level questions to analyze model data . |
| Outcome: | LLMEval-Fair provides robust and credible evaluation framework for Large Language Models . it provides a strong empirical validation for the dynamic evaluation paradigm . |
Probing the Safety Robustness of LLMs in Latent Space (2026.acl-long)
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Tianle Gu, Kexin Huang, Zongqi Wang, Yixu Wang, Jie Li, Xin Wang, Yang Yao, Yujiu Yang, Yan Teng, Yingchun Wang
| Challenge: | Despite substantial progress in safety alignment techniques, aligned large language models can still produce unsafe responses under minor internal perturbations. |
| Approach: | They introduce Activation Steering Attack (ASA) and leverage the Negative Log-Likelihood (NLL) as a diagnostic signal to probe the local sensitivity of safety behaviors in latent space. |
| Outcome: | The proposed method is model-agnostic and supervision-free, enabling a general and reproducible diagnostic metric for analyzing safety robustness. |
A Survey of Large Language Models for Text-Guided Molecular Discovery: From Molecule Generation to Optimization (2026.acl-long)
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| Challenge: | Large language models (LLMs) are introducing a paradigm shift in molecular discovery by enabling text-guided interaction with chemical spaces through natural language and symbolic notations. |
| Approach: | They analyze the current LLM learning paradigms to tackle four critical evaluation dimensions that have emerged as critical dimensions in recent studies. |
| Outcome: | The proposed models are able to interact with chemical spaces through natural language and symbolic notations, and have emerging extensions to incorporate multi-modal inputs. |
GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval (2022.naacl-main)
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| Challenge: | Dense retrieval approaches suffer from the lexical gap and require large amounts of training data. |
| Approach: | They propose an unsupervised method for domain adaptation that uses query generator and pseudo labeling from a cross-encoder to improve retrieval performance. |
| Outcome: | The proposed method outperforms state-of-the-art retrieval methods on domain-specialized datasets by 9.3 points nDCG@10 on six tasks. |
From Evasion to Concealment: Stealthy Knowledge Unlearning for LLMs (2025.findings-acl)
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| Challenge: | Existing approaches to unlearning often treat nonsensical responses or template-based refusals as the unlearning target, making the process even more vulnerable to attacks and jailbreaks. |
| Approach: | They propose a method that uses inverted facts to remove the need for auxiliary models or retaining data while avoiding leakage. |
| Outcome: | Evaluated on the ToFU Knowledge Unlearning dataset using Llama2-7B-Chat and Phi-1.5, MEOW outperforms baselines in forgetting quality while preserving model utility. |
DAPR: A Benchmark on Document-Aware Passage Retrieval (2024.acl-long)
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| Challenge: | Recent approaches to retrieve short passages are limited due to expensive operations such as selfattention. |
| Approach: | They propose to use a task to find passages from a large corpus of documents . they extend the SoTA passage retrievers with document context to achieve this task . |
| Outcome: | The proposed task is called Document-Aware Passage Retrieval (DAPR) it performs best on easy and hard queries, but fails on hard queries requiring document-context understanding . |
MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents (2026.acl-long)
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Pengxiang Zhao, Guangyi Liu, Yaozhen Liang, Weiqing He, Zhengxi Lu, WenHao Wang, Yuehao Huang, Yuxiang Chai, Zhaolu Kang, Yaxuan Guo, Hao Wang, Kexin Zhang, Liang Liu, Yong Liu
| Challenge: | Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, but systematic evaluation of GUI–shortcut hybrid agents remains underexplored. |
| Approach: | They propose a benchmark that evaluates GUI-shortcut hybrid agents with a specific focus on the mobile domain. |
| Outcome: | MAS-Bench evaluates agent's ability to generate shortcuts by discovering and creating reusable, low-cost workflows. |
Modular Sentence Encoders: Separating Language Specialization from Cross-Lingual Alignment (2025.acl-long)
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| Challenge: | Multilingual sentence encoders are often trained to map sentences from different languages into a shared semantic vector space . cross-lingual alignment training distorts optimal monolingual structure of semantic spaces of individual languages . a modular solution can be used for cross-linguistic tasks such as cross-language semantic similarity and zero-shot transfer . |
| Approach: | They propose a modular training system that embeds sentences from different languages into a shared semantic vector space. |
| Outcome: | The proposed solution achieves better performance across all tasks compared to monolithic models. |
ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models (2024.emnlp-main)
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Haiquan Zhao, Lingyu Li, Shisong Chen, Shuqi Kong, Jiaan Wang, Kexin Huang, Tianle Gu, Yixu Wang, Jian Wang, Liang Dandan, Zhixu Li, Yan Teng, Yanghua Xiao, Yingchun Wang
| Challenge: | Emotion Support Conversation (ESC) is a crucial application for reducing stress and providing emotional guidance. |
| Approach: | They re-organize 2,801 role-playing cards to define roles of role-players . they train a specific role- playing model called ESC-Role which behaves more like a confused person than GPT-4 . |
| Outcome: | The proposed model behaves more like a confused person than GPT-4, and the model performs better than GPLs. |
UKP-SQUARE: An Online Platform for Question Answering Research (2022.acl-demo)
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Tim Baumgärtner, Kexin Wang, Rachneet Sachdeva, Gregor Geigle, Max Eichler, Clifton Poth, Hannah Sterz, Haritz Puerto, Leonardo F. R. Ribeiro, Jonas Pfeiffer, Nils Reimers, Gözde Şahin, Iryna Gurevych
| Challenge: | Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats and require different model architectures and setups. |
| Approach: | They propose an extensible online QA platform that allows users to query and analyze a large collection of modern Skills via a user-friendly web interface and integrated behavioural tests. |
| Outcome: | The proposed tool allows users to query and analyze a large collection of modern Skills via a user-friendly web interface and integrated behavioural tests. |
Conflict-Aware Memory for Embodied Agents: Enhancing Vector Data Quality via Detection Rules (2026.acl-long)
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| Challenge: | Embodied agents have successfully leveraged large language models (LLMs) to better transform human instructions and images into executable task plans. |
| Approach: | They propose Conflict Detection Rules to identify and manage data quality issues in vector knowledge bases and correct the index structure. |
| Outcome: | Experimental results show that planners with Conflict Detection Rules exceed the basic LLM planner by 15.25% and 14.25% in grammatical accuracy (GA) and interpretation accuracy (IA) on average. |
GameplayQA: A Benchmarking Framework for Decision-Dense POV-Synced Multi-Video Understanding of 3D Virtual Agents (2026.acl-long)
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Yunzhe Wang, Runhui Xu, Kexin Zheng, Tianyi Zhang, Jayavibhav Niranjan Kogundi, Soham Hans, Volkan Ustun
| Challenge: | Multimodal Large Language Models are emerging as a backbone for autonomous agents in 3D environments. |
| Approach: | They propose a framework for evaluating agentic-centric perception and reasoning through video understanding. |
| Outcome: | The proposed framework evaluates agentic-centric perception and reasoning through video understanding. |
Measuring Social Norms of Large Language Models (2024.findings-naacl)
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| Challenge: | Existing datasets that evaluate a general understanding of social science are inadequate to understand social norms. |
| Approach: | They propose a multi-agent framework to improve large language models’ ability to understand social norms by comparing them to elementary students. |
| Outcome: | The proposed framework improves large language models to be on par with humans. |
AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot Classification (2023.emnlp-main)
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| Challenge: | Recent work has found that few-shot sentence classification based on pre-trained Sentence Encoders (SEs) is efficient, robust, and effective. |
| Approach: | They propose a domain-specialization approach that decouples SEPT from DAPT by training a SEPT adapter on a pre-trained PLM. |
| Outcome: | The proposed approach matches or surpasses the performance of full SEPT on DAPT-ed PLMs while significantly reducing training costs. |
Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs (2024.findings-emnlp)
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| Challenge: | Existing research often overlooks the data quality issues within retrieval results, often caused by inaccurate existing vector-distance-based retrieval methods. |
| Approach: | They propose to use Context-Driven Index Trimming framework to capture and regulate consistency between retrieved contexts and modify indexes in the database. |
| Outcome: | Experiments show that the proposed framework can improve answer quality by 3.75% on open-domain question-answering tasks. |
UKP-SQuARE v3: A Platform for Multi-Agent QA Research (2023.acl-demo)
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Haritz Puerto, Tim Baumgärtner, Rachneet Sachdeva, Haishuo Fang, Hao Zhang, Sewin Tariverdian, Kexin Wang, Iryna Gurevych
| Challenge: | Current approaches to QA models are multi-dataset models, but combining expert agents can yield large performance gains over multi-agent models. |
| Approach: | They extend an online platform for QA research to support three families of multi-agent systems: agent selection, early-fusion of agents, and late-fusion. |
| Outcome: | The proposed model can be compared with multi-dataset models and achieve high inference speed and performance. |
SpikeVoice: High-Quality Text-to-Speech Via Efficient Spiking Neural Network (2024.acl-long)
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| Challenge: | SpikeVoice performs high-quality Text-To-Speech (TTS) via SNN . major obstacle to using SNN for such generative tasks lies in the demand for models to grasp long-term dependencies. |
| Approach: | They propose a brain-inspired Spiking Neural Network (SNN) which performs high-quality Text-To-Speech (TTS) via SNN and explores the potential of SNN to "speak". |
| Outcome: | The proposed model achieves comparable results to Artificial Neural Networks (ANN) with only 10.5% energy consumption of ANN. |