Papers by Ao Wang
Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Spatial Reasoning (2025.findings-emnlp)
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Yihong Tang, Ao Qu, Zhaokai Wang, Dingyi Zhuang, Zhaofeng Wu, Wei Ma, Shenhao Wang, Yunhan Zheng, Zhan Zhao, Jinhua Zhao
| Challenge: | Currently, vision-language models excel in many downstream tasks but struggle with spatial reasoning, which is crucial for navigation and interaction with physical environments. |
| Approach: | They propose a framework that generates synthetic data to provide targeted supervision for VLMs across these basic spatial capabilities. |
| Outcome: | The proposed framework disentangles 2D spatial reasoning into three core components: direction comprehension, distance estimation, and localization. |
Multimodal Causal Reasoning Benchmark: Challenging Multimodal Large Language Models to Discern Causal Links Across Modalities (2025.findings-acl)
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| Challenge: | Existing MLLMs lack robustness in multimodal causal reasoning compared to their performance in textual settings. |
| Approach: | They propose a novel multimodal chain-of-thought (CoT) reasoning benchmark that leverages siamese images and text pairs to challenge MLLMs. |
| Outcome: | The proposed benchmark leverages siamese images and text pairs to challenge MLLMs. |
Focusing Condition: Inference-Time Self-Contrastive Steering Elicits Better Conditional Text Embeddings in LLMs (2026.acl-long)
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| Challenge: | Existing methods for extracting conditional text embeddings from large language models (LLMs) relying on prompts often fails to produce high-quality conditional embeddables, resulting in degradation of quality. |
| Approach: | They propose a plug-and-play method that constructs unconditional general text embeddings and uses them to refine conditional text embeds. |
| Outcome: | The proposed method improves performance of prompt-based methods on clustering, Semantic Textual Similarity, and triplet alignment datasets. |
Prompt Tuning for Discriminative Pre-trained Language Models (2022.findings-acl)
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Yuan Yao, Bowen Dong, Ao Zhang, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Leyu Lin, Maosong Sun, Jianyong Wang
| Challenge: | Recent studies have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing tasks. |
| Approach: | They propose a prompt tuning framework that reformulates NLP tasks into a discriminative language modeling problem. |
| Outcome: | The proposed framework improves on text classification and question answering tasks and prevents unstable tuning problems in low-resource settings. |
Enhancing the Transferability of Jailbreak Attacks on Large Language Models via Exploiting Reparameterization Invariance (2026.acl-long)
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| Challenge: | Existing token-level attacks have shown efficacy on open-source models but suffer from poor cross-model transferability. |
| Approach: | They propose a framework to improve cross-model transferability by modifying model parameters and generating update directions according to differences in output distributions rather than parameter-space distances. |
| Outcome: | The proposed framework improves cross-model transferability and success rates on open-source models. |
Semi-Supervised Formality Style Transfer with Consistency Training (2022.acl-long)
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| Challenge: | Formality style transfer (FST) is a task that involves paraphrasing an informal sentence into a formal one without altering its meaning. |
| Approach: | They propose a semi-supervised framework to better utilize source-side unlabeled sentences based on consistency training. |
| Outcome: | The proposed framework can achieve state-of-the-art results even with less than 40% of the parallel data. |
Adaptive Immune-based Sound-Shape Code Substitution for Adversarial Chinese Text Attacks (2024.emnlp-main)
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| Challenge: | Existing text attack methods are designed for English text, but robust implementation of Chinese text is understudied. |
| Approach: | They propose an adaptive immune-based sound-shape code algorithm for Chinese text attacks . they leverage the Sound-Shape Code to generate natural substitutions . |
| Outcome: | The proposed algorithm produces high-quality Chinese adversarial examples . it can reduce duplication of population and improve search ability . |
Distillation with Explanations from Large Language Models (2024.lrec-main)
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| Challenge: | Large language models (LLMs) like ChatGPT and GPT-4 have made remarkable progress in various NLP tasks while also providing explanations alongside their answers. |
| Approach: | They propose to use Large language models (LLMs) to generate more accurate answers and corresponding free-text explanations by combining ground truth labels and answers-explanations generated by LLMs. |
| Outcome: | The proposed method achieves improved predictive performance and generates explanations that exhibit greater alignment with the model’s task outputs. |
CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have a global audience, so alignment must extend to cultural resonance. |
| Approach: | They propose a framework that frames alignment as a conditional capacity separation problem. |
| Outcome: | The proposed framework outperforms both dense baselines and semantic-only MoEs on three large language models. |
HSGraphAgent: Knowledge-Graph-Guided Large Language Models for Harmonized System Code Classification (2026.acl-long)
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| Challenge: | Harmonized System (HS) code classification is a hierarchically structured and regulation-constrained task, often complicated by short and noisy product descriptions. |
| Approach: | They propose a knowledge-graph-guided LLM framework that formulates HS classification as a stepwise, regulation-aware reasoning process over an explicit HS knowledge graph. |
| Outcome: | The proposed framework constrains inference to legally valid paths while producing explicit and traceable reasoning trajectories. |
FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling (2025.acl-long)
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Weilin Zhao, Tengyu Pan, Xu Han, Yudi Zhang, Sun Ao, Yuxiang Huang, Kaihuo Zhang, Weilun Zhao, Yuxuan Li, Jie Zhou, Hao Zhou, Jianyong Wang, Maosong Sun, Zhiyuan Liu
| Challenge: | Speculative sampling is an efficient way to accelerate the auto-regressive generation process of large language models. |
| Approach: | They propose a frequency-ranked speculative sampling framework that optimizes draft candidate selection through vocabulary space compression. |
| Outcome: | Experiments show that FR-Spec reduces LM Head computation overhead by 75% while ensuring the equivalence of the final output distribution. |
Generative Data Augmentation for Aspect Sentiment Quad Prediction (2023.starsem-1)
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| Challenge: | Existing approaches to analyze text contain rewrites and inconsistency between text and quads. |
| Approach: | They propose a new approach to analyze aspect terms, opinion terms, sentiment polarity in text . they augment quads and train a quads-to-text model to generate corresponding texts . |
| Outcome: | The proposed method outperforms existing methods and achieves state-of-the-art performance on two datasets. |
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs (2025.findings-acl)
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| Challenge: | Existing methods for retrieval-augmented generation struggle with a trade-off between flexibility and retrieval quality. |
| Approach: | They propose a flexible modular KG-RAG framework that uses query text instead of KGs . they propose to use query text to infer the structural information of reasoning paths . |
| Outcome: | The proposed method achieves state-of-the-art performance with high efficiency and low resource consumption. |
ViDove: A Translation Agent System with Multimodal Context and Memory-Augmented Reasoning (2025.emnlp-demos)
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Yichen Lu, Wei Dai, Jiaen Liu, Ching Wing Kwok, Zongheng Wu, Xudong Xiao, Ao Sun, Sheng Fu, Jianyuan Zhan, Yian Wang, Takatomo Saito, Sicheng Lai
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities in Machine Translation (MT) tasks. |
| Approach: | They propose a translation agent system designed for multimodal input that leverages visual and contextual background information to enhance the translation process. |
| Outcome: | The proposed translation agent achieves significantly higher translation quality in subtitle generation and general translation tasks compared to previous state-of-the-art systems. |
MERIT: Multi-Agent Collaboration for Unsupervised Time Series Representation Learning (2025.findings-acl)
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| Challenge: | Existing approaches to time series representation learning are time-consuming and expert-dependent, which are difficult to generalize across different tasks. |
| Approach: | They propose to use large language model agent to guide unsupervised time series representation learning and a framework to integrate three LLM agents to collaboratively generate positive views for time series data. |
| Outcome: | The proposed framework integrates large language model (LLM) agent to guide unsupervised time series representation learning and compares it with state-of-the-art baselines on multiple time series datasets. |
PENS: A Dataset and Generic Framework for Personalized News Headline Generation (2021.acl-long)
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| Challenge: | Using a dataset of Microsoft News, we propose a generic framework to personalize a text generator and establish personalized headlines. |
| Approach: | They propose a generic framework to personalize a news headline generator and establish personalized headlines by leveraging user behavioral data. |
| Outcome: | The proposed framework is based on user preference data and user preference injections to personalize a text generator and establish personalized headlines. |
DuSQL: A Large-Scale and Pragmatic Chinese Text-to-SQL Dataset (2020.emnlp-main)
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| Challenge: | Existing text-to-SQL parsing methods mainly focus on English, but there is no labeled data available for the language . a larges-scale and pragmatic Chinese dataset is used for cross-domain text- to-Sql task . |
| Approach: | They propose a larges-scale Chinese dataset for a cross-domain text-to-SQL task . they analyze questions from several representative applications and use an effective data construction framework . |
| Outcome: | The proposed dataset contains 200 databases, 813 tables, and 23,797 question/SQL pairs. |
Modelling Long-distance Node Relations for KBQA with Global Dynamic Graph (2020.coling-main)
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| Challenge: | Existing studies rely on deep graph neural networks (GNNs) to capture rich structural information, but they lack the structural information needed for QA. |
| Approach: | They propose a framework which captures structural information from KBs and models long-distance node relations from two perspectives. |
| Outcome: | The proposed framework models long-distance node relations from two perspectives . it is based on two widely used multi-hop KBQA datasets . |
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing (2021.emnlp-main)
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| Challenge: | Existing approaches to parse text-to-SQL data are lacking labeled data for unseen evaluation databases. |
| Approach: | They propose a framework for enhancing SQL queries by automatically producing large numbers of SQL queries based on an abstract syntax tree grammar. |
| Outcome: | The proposed framework can produce high-quality natural language questions over strong baselines. |
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing (2022.acl-long)
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Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei
| Challenge: | Existing work shows that pre-trained models can improve in various natural language processing tasks. |
| Approach: | They propose a unified-modal encoder-decoder framework that pre-trains speech-text representations using large-scale unlabeled speech and text data. |
| Outcome: | The proposed framework is superior to existing models on speech-to-text processing tasks. |
AEA: Adaptive Expert Allocation Improves Sentence Embeddings from Mixture-of-Experts LLM (2026.acl-long)
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| Challenge: | Existing methods to improve embeddings from Mixture-of-Experts models allocate a fixed number of experts uniformly across all layers and tokens, ignoring inter-layer and inter-token heterogeneity. |
| Approach: | They propose an Adaptive Expert Allocation framework that performs layer-wise and token-wise expert allocation to enhance embedding quality. |
| Outcome: | The proposed method improves embedding quality across multiple MoE models. |
ItiNera: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning (2024.emnlp-industry)
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Yihong Tang, Zhaokai Wang, Ao Qu, Yihao Yan, Zhaofeng Wu, Dingyi Zhuang, Jushi Kai, Kebing Hou, Xiaotong Guo, Jinhua Zhao, Zhan Zhao, Wei Ma
| Challenge: | Existing urban itinerary planning studies focus on traditional tourism, but they lack the precision and accuracy needed to create a personalized itinerary. |
| Approach: | They propose an open-domain urban itinerary planning system that integrates spatial optimization with large language models to provide customized urban itineraries based on user needs. |
| Outcome: | The proposed system can generate personalized urban itineraries based on user needs and scale with existing methods. |
Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction (2022.coling-1)
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Yidong Wang, Hao Wu, Ao Liu, Wenxin Hou, Zhen Wu, Jindong Wang, Takahiro Shinozaki, Manabu Okumura, Yue Zhang
| Challenge: | Existing methods to extract opinion words from sentences are limited due to the expensive annotation process. |
| Approach: | They propose to exploit massive unlabeled data to reduce distribution shift risk . they propose to use two filters specifically for TOWE to filter noisy data . results indicate superiority of MGCR over current state-of-the-art methods . |
| Outcome: | The proposed method reduces the risk of distribution shifts by increasing the exposure of the model to varying distribution shift. |
Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation (P19-1)
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| Challenge: | Existing generative methods overlook grammatical structure or make factual mistakes in generated texts. |
| Approach: | They propose a template-based method to ensure the readability of generated type descriptions . they also propose measurable metrics to measure the readibility of the generated type description . |
| Outcome: | The proposed method improves substantially compared with baselines and achieves state-of-the-art performance on both datasets. |
Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge (2020.acl-main)
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| Challenge: | Chinese word segmentation and part-of-speech tagging are important fundamental tasks in natural language processing. |
| Approach: | They propose a neural model for Chinese word segmentation and part-of-speech tagging . they incorporate context features and syntactic knowledge for each input character . |
| Outcome: | The proposed model can learn and benefit from existing tools, but its quality may be poor. |