Papers by Jun Zhu
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension (2023.findings-emnlp)
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| Challenge: | X-STA is a new approach for cross-lingual machine reading comprehension . the variation of answer span positions in different languages makes it difficult to transfer knowledge across languages. |
| Approach: | They propose a method that leverages an attentive teacher to subtly transfer the answer spans of the source language to the answer output space of the target. |
| Outcome: | The proposed method outperforms state-of-the-art approaches on three multi-lingual datasets. |
Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do (2026.acl-long)
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| Challenge: | Existing open-source models often yield only marginal overall improvements, possibly due to an overemphasis on mathematical reasoning at the expense of broader capabilities. |
| Approach: | They evaluate 12 multimodal tasks using 14 non-reasoning models and 8 reasoning models. |
| Outcome: | The proposed method is effective in multimodal reasoning tasks, the authors show . they show that it lacks the ability to maintain deep visual introspection throughout the reasoning process. |
GeoLaux: A Benchmark for Evaluating MLLMs’ Geometry Performance on Long-Step Problems Requiring Auxiliary Lines (2026.acl-long)
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| Challenge: | Existing benchmarks for Geometry problem solving lack fine-grained evaluation for long-step problems necessitating auxiliary line construction. |
| Approach: | They present a fine-grained annotated dataset with long-step reasoning and auxiliary line construction that provides a detailed evaluation of 23 leading MLLMs. |
| Outcome: | The proposed model performs significantly worse on long-step problems than short-step ones, with 18 models showing a performance drop of over 50%. |
Complex Numerical Reasoning with Numerical Semantic Pre-training Framework (2025.emnlp-main)
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| Challenge: | Numerical knowledge graphs (NKGs) are not limited to discrete entity-relation knowledge. |
| Approach: | They propose to combine numerical values and entities to solve multi-hop complex reasoning over incomplete knowledge graphs. |
| Outcome: | The proposed approach handles up to 102 types of complex numerical reasoning queries on three public datasets. |
LlmLink: Dual LLMs for Dynamic Entity Linking on Long Narratives with Collaborative Memorisation and Prompt Optimisation (2025.coling-main)
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| Challenge: | Existing methods focus on supervised fine-tuning or limited to one-off prediction, which poses a challenge where the context is long. |
| Approach: | They propose a dynamic approach to CoREFerence resolution in chunked long narratives by deploying dual Large Language Models. |
| Outcome: | The proposed model achieves performance gains over existing models and fine-tuning approaches on long narrative datasets, significantly reducing the resources required for inference and training. |
Tailoring Vaccine Messaging with Common-Ground Opinions (2024.findings-naacl)
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Rickard Stureborg, Sanxing Chen, Roy Xie, Aayushi Patel, Christopher Li, Chloe Zhu, Tingnan Hu, Jun Yang, Bhuwan Dhingra
| Challenge: | Vaccine interventions aim to answer concerns expressed about vaccination. |
| Approach: | They propose a dataset to evaluate how well responses are tailored to a common-ground opinion . they find that GPT-4-Turbo performs significantly better than others . |
| Outcome: | The proposed dataset outperforms fine tuned LLMs on the task of tailoring vaccine responses to common-ground opinions. |
Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs (2025.findings-acl)
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| Challenge: | Existing prompt optimization methods rely on extensive manual effort or meta-cognitive abilities, making them less effective for LwLLMs. |
| Approach: | They propose a direct behavior optimization parameter that transforms the optimization of complex prompts into discrete, quantifiable execution sequences using a gradient-free Monte Carlo Tree Search. |
| Outcome: | The proposed method outperforms current prompt optimization methods on seven challenging tasks where state-of-the-art LLMs excel but LwLLMs generally underperform. |
Importance of Synthesizing High-quality Data for Text-to-SQL Parsing (2023.findings-acl)
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Yiqun Hu, Yiyun Zhao, Jiarong Jiang, Wuwei Lan, Henghui Zhu, Anuj Chauhan, Alexander Hanbo Li, Lin Pan, Jun Wang, Chung-Wei Hang, Sheng Zhang, Jiang Guo, Mingwen Dong, Joseph Lilien, Patrick Ng, Zhiguo Wang, Vittorio Castelli, Bing Xiang
| Challenge: | Existing text-to-SQL parsers lack the data to perform well with augmented synthetic data. |
| Approach: | They propose a framework that imposes strong typing constraints and incorporates key relationships from schema. |
| Outcome: | The proposed framework improves on the high-quality synthesized SQL and natural language question (NLQ) models have significant accuracy boosts and achieve new state-of-the-art performance on spider. |
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. |
HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models (2026.findings-acl)
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| Challenge: | Existing methods for visual token pruning lack insight into the intrinsic property of the vision encoder . et al., 2017: 99.3% of task accuracy with only 1/3 of the tokens. |
| Approach: | They propose a model-agnostic token pruning method that trains without training . they propose 'HiPrune' method which prunes visual tokens according to their attention . |
| Outcome: | The proposed method achieves 99.3% of task accuracy with only 1/3 of the tokens . it reduces inference FLOPs by 58.7% and maintains 99.99% accuracy with 2/9 tokens. |
Benchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning (2025.acl-long)
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| Challenge: | Existing methods for enhancing understanding and reasoning abilities in graphbased tasks focus on specific graph types or tasks, posing challenges in designing versatile systems suitable for various tasks and graphs across diverse domains. |
| Approach: | They propose a structure-aware fine-tuning framework to enhance LVLMs with structure learning abilities through three self-supervised learning tasks. |
| Outcome: | Extensive evaluations on 14 LVLMs reveal that LVLs are weak in basic graph understanding and reasoning tasks, particularly those concerning relational or structurally complex information. |
InquireMobile: Teaching VLM-based Mobile Agent to Request Human Assistance via Reinforcement Fine-Tuning (2026.acl-long)
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Qihang Ai, Pi Bu, Yue Cao, Yingyao Wang, Jihao Gu, Jingxuan Xing, Zekun Zhu, Wei Jiang, Zhicheng Zheng, Jun Song, Yuning Jiang
| Challenge: | Recent advances in Vision-Language Models (VLMs) have enabled mobile agents to perceive and interact with real-world mobile environments based on human instructions. |
| Approach: | They propose a vision-language model that actively seeks human confirmation at critical decision points and a model inspired by reinforcement learning. |
| Outcome: | The proposed model achieves an improvement of 46.8% in inquiry success rate and the best overall success rate among existing baselines on InquireBench. |
Robust Preference Optimization via Dynamic Target Margins (2025.findings-acl)
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| Challenge: | Direct Preference Optimization (DPO) is an efficient method for ensuring safety and reliability in practical applications. |
| Approach: | They propose a dynamic target margin preference optimization algorithm that adjusts reward margins at the pairwise level. |
| Outcome: | The proposed method achieves an average 4.4% improvement over baselines, setting new benchmarks for state-of-the-art performance. |
Let’s Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models (2024.lrec-main)
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| Challenge: | Empirical evaluations conducted on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
| Approach: | They propose a diffusion model which extracts aspects step by step and learns a denoising process that progressively restores them in a reverse manner. |
| Outcome: | Empirical evaluations on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
Joint Semantic and Strategy Matching for Persuasive Dialogue (2023.findings-emnlp)
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Chuhao Jin, Yutao Zhu, Lingzhen Kong, Shijie Li, Xiao Zhang, Ruihua Song, Xu Chen, Huan Chen, Yuchong Sun, Yu Chen, Jun Xu
| Challenge: | Persuasive dialogue models rely on utterance semantic matching and a key aspect has been ignored . compared with utterrance semantics, conversation strategies are high-level concepts, which can be informative and provide complementary information to achieve effective persuation. |
| Approach: | They propose to model conversation semantics and strategies to match them using a BERT-like module and an auto-regressive predictor. |
| Outcome: | The proposed model improves state-of-the-art by 5% on a small and 37% on 'large' datasets. |
A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction (2024.lrec-main)
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| Challenge: | Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from unstructured document. |
| Approach: | They propose a document-prompt-based method for document-level event argument extraction that uses a semantic mention graph to capture relations between documents and prompts. |
| Outcome: | The proposed method surpasses baseline methods and achieves state-of-the-art performance on RAMS and WikiEvents datasets. |
A Diffusion Model for Event Skeleton Generation (2023.findings-acl)
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| Challenge: | Existing methods for event schema generation are noise-sensitive and error-accumulating, e.g., inability to correct errors while generating schema. |
| Approach: | They propose a novel diffusion event graph model that embeds and roundes event graphs into learnable latent representations and a denoising process to maintain the model's robustness. |
| Outcome: | The proposed model achieves better results than existing state-of-the-art models on three IED bombing datasets. |
See the World, Discover Knowledge: A Chinese Factuality Evaluation for Large Vision Language Models (2025.findings-acl)
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Jihao Gu, Yingyao Wang, Pi Bu, Chen Wang, Ziming Wang, Tengtao Song, Donglai Wei, Jiale Yuan, Yingxiu Zhao, Yancheng He, Shilong Li, Jiaheng Liu, Meng Cao, Jun Song, Yingshui Tan, Xiang Li, Wenbo Su, Xiaoyong Zhu, Bo Zheng
| Challenge: | Existing models for large vision language models do not fully reflect their knowledge capacity and reliability, resulting in erroneous outputs that do not align with the image content or provide answers lacking knowledge evidence. |
| Approach: | They propose a Chinese-based benchmark for visual factuality across 8 major topics and 56 subtopics and a multi-hop question construction. |
| Outcome: | The proposed model decouples visual factuality into two parts: seeing the world and discovering knowledge. |
ReEvalMed: Rethinking Medical Report Evaluation by Aligning Metrics with Real-World Clinical Judgment (2025.emnlp-main)
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| Challenge: | Automatically generated radiology reports often receive high scores from existing evaluation metrics but fail to earn clinicians’ trust. |
| Approach: | They propose a meta-evaluation framework that uses criteria spanning discrimination, robustness, and monotonicity to evaluate existing metrics. |
| Outcome: | The proposed framework offers guidance for building more clinically reliable evaluation methods. |
PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models (2026.acl-long)
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| Challenge: | Large Language Models lack the capacity to formulate global strategies due to latency and availability constraints. |
| Approach: | They propose a framework to internalize the strategic oversight of large models into intrinsic Latent Guidance by synthesizing a query-conditioned Latent Guide. |
| Outcome: | The proposed framework outperforms strong baselines on mathematical and coding benchmarks with negligible inference latency. |
ControlAudio: Tackling Text-Guided, Timing-Indicated and Intelligible Audio Generation via Progressive Diffusion Modeling (2026.acl-long)
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| Challenge: | Recent efforts on text-to-audio generation are exploring fine-grained controllability . however, their performance at scale is limited due to data scarcity . |
| Approach: | They propose a multi-task learning problem for high-controllability text-to-audio generation . they propose scalable diffusion transformers that augment condition information in sequence . |
| Outcome: | The proposed method outperforms existing methods on objective and subjective evaluations. |
Adversarial Alignment with Anchor Dragging Drift (A3D2): Multimodal Domain Adaptation with Partially Shifted Modalities (2025.acl-long)
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| Challenge: | Domain adaptation is underexplored in multimodal learning environments due to expensive data collection and annotation. |
| Approach: | They propose a bi-alignment scheme to perform drift-drift and anchor-driving matching with partially shifting anchors. |
| Outcome: | The proposed approach achieves superior performance compared with state-of-the-art approaches. |
Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing (2022.acl-short)
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| Challenge: | Text-to-SQL parsing aims to parse natural language questions into SQL queries . current attention-based approaches can only model alignments at the token level . |
| Approach: | They propose a method to leverage explicit lexico-logical alignments by identifying possible phrase-level alignments and injecting them as additional contexts into the parsing procedure. |
| Outcome: | The proposed approach improves performance by 3.4% on Squall. |
HICD: Hallucination-Inducing via Attention Dispersion for Contrastive Decoding to Mitigate Hallucinations in Large Language Models (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) often generate hallucinations, producing outputs that are contextually inaccurate or factually incorrect. |
| Approach: | They propose a method that selects attention heads crucial to the model's prediction as inducing heads and induces hallucinations by dispersing attention of these inducers. |
| Outcome: | The proposed method significantly improves performance on tasks requiring contextual faithfulness, reading comprehension, and question answering. |
ARTIST: A Transformer-based Chinese Text-to-Image Synthesizer Digesting Linguistic and World Knowledge (2022.findings-emnlp)
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| Challenge: | Text-to-Image Synthesis (TIS) is a popular task to convert natural language texts into realistic images. |
| Approach: | They propose a transformer-based Chinese text-to-image synthesizer for high-resolution image generation that incorporates linguistic and relational knowledge facts into the model to ensure better performance without the usage of ultra-large models. |
| Outcome: | The proposed model outperforms existing models in Chinese with linguistic and relational knowledge facts. |
CNNSum: Exploring Long-Context Summarization with Large Language Models in Chinese Novels (2025.findings-acl)
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| Challenge: | Currently, long-context summarization mainly relies on memory ability. |
| Approach: | They propose a multi-scale long-context summarization benchmark based on Chinese novels . they use human-driven annotations to analyze long-constituency models . |
| Outcome: | The proposed benchmark features human-driven annotations across four subsets with lengths ranging from 16k to 128k. |
Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems (2022.acl-long)
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| Challenge: | Existing work on empathetic dialogues focused on the two-party scenario, but multi-party dialogues are pervasive in reality. |
| Approach: | They propose a multi-party empathetic dialogue generation task that uses a static-dynamic model to explore emotion and sensibility. |
| Outcome: | The proposed task is based on a model with static sensibility and dynamic emotion . it achieves state-of-the-art performance in multi-party empathetic dialogue learning . |
PSC: Extending Context Window of Large Language Models via Phase Shift Calibration (2024.emnlp-main)
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| Challenge: | Large-scale language models (LLMs) have shown impressive results across a variety of tasks. |
| Approach: | They propose a module for calibrating the frequencies predefined by existing methods . they conducted extensive experiments across multiple models and tasks . |
| Outcome: | The proposed method reduces perplexity as the context window size is varied from 16k to 32k and up to 64k. |
Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? (2024.acl-long)
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| Challenge: | Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections. |
| Approach: | They propose a co-temporal Question Answering benchmark that contains four co-time scenarios with 4,748 samples for evaluating the co-timing abilities of large language models. |
| Outcome: | The proposed benchmarks show that current LLMs struggle on CoTempQA tasks even when enhanced with Chain of Thought methodologies. |
LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models (2026.acl-long)
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Fengqi Zhu, Rongzhen Wang, Shen Nie, Xiaolu Zhang, Chunwei Wu, Jun Zhou, Yankai Lin, Ji-Rong Wen, Chongxuan Li
| Challenge: | Masked diffusion language models have achieved significant progress in language modeling . however, the systematic analysis and empirical validation of their alignment on general tasks remains underexplored. |
| Approach: | They propose a framework that analyzes the bias and variance of preference optimization loss and gradient based on Direct Preference Optimization. |
| Outcome: | The proposed model outperforms its SFT-only predecessor on general benchmarks . it consistently outperformed other strong language models and ARMs on general tasks . |
Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)
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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen
| Challenge: | Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. |
| Approach: | They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
| Outcome: | The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database (2024.lrec-main)
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| Challenge: | Existing benchmarks for textual question answering only focus on single-chain or single-hop retrieval . Existing approaches to answer complex questions have limitations . |
| Approach: | They propose to conduct Graph-Hop, a novel multi-chains and multi-hops retrieval paradigm in complex question answering. |
| Outcome: | The proposed model provides explicit and fine-grained evidence graphs for complex question to support comprehensive and detailed reasoning. |
Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning (2022.findings-naacl)
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| Challenge: | a lack of nodes in the job title representation graph hinders further analysis . a new approach to learn job title represents the job for short . |
| Approach: | They propose to embed nodes that improve the quality of job title representation . they construct a heterogeneous graph with job titles and tags . |
| Outcome: | The proposed method improves the quality of job title representation on two datasets. |
Let Modalities Teach Each Other: Modal-Collaborative Knowledge Extraction and Fusion for Multimodal Knowledge Graph Completion (2025.findings-naacl)
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| Challenge: | Recent studies have focused on missing triples in knowledge graphs, but lack correlation between modalities. |
| Approach: | They propose a framework to foster mutual guidance and collaboration in unimodal knowledge extraction and multimodal knowledge fusion. |
| Outcome: | Extensive experiments on three real-world datasets demonstrate advantages of Moodle over state-of-the-art methods. |
MMNMT: Modularizing Multilingual Neural Machine Translation with Flexibly Assembled MoE and Dense Blocks (2023.emnlp-main)
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| Challenge: | Mixture-of-Experts (MoE) based sparse architectures are prone to overfitting on low-resource language translation. |
| Approach: | They propose a modularized MNMT framework that flexibly assembles dense and MoE-based sparse modules to achieve the best of both worlds. |
| Outcome: | The proposed framework outperforms existing models on low-resource language translation and zero-shot translation on benchmark datasets. |
LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement (2025.acl-long)
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Boyi Kang, Xinfa Zhu, Zihan Zhang, Zhen Ye, Mingshuai Liu, Ziqian Wang, Yike Zhu, Guobin Ma, Jun Chen, Longshuai Xiao, Chao Weng, Wei Xue, Lei Xie
| Challenge: | Recent advances in language models have demonstrated strong capabilities in semantic understanding and contextual modeling. |
| Approach: | They propose a LLaMA-based language model that incentivizes generalization capabilities for speech enhancement. |
| Outcome: | The proposed language model outperforms prior task-specific discriminative and generative models in acoustic enhancement tasks. |
SGDPO: Self-Guided Direct Preference Optimization for Language Model Alignment (2025.findings-acl)
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| Challenge: | Existing methods for aligning Large Language Models with human values are limited and results of DPO are not resilient. |
| Approach: | They propose a self-guided direct preference optimization algorithm that incorporates a pilot term to steer the gradient flow during the optimization process. |
| Outcome: | The proposed method can generate human-preferred response up to 9.19% higher than previous methods. |
Language Modeling with Sparse Product of Sememe Experts (D18-1)
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| Challenge: | Existing language modeling methods rely on large-scale text data to learn the sequential patterns of words. |
| Approach: | They propose to use sememes to represent the implicit semantics behind words for language modeling . they propose to employ sememe-driven language models to fine-grained semem-level semantics . |
| Outcome: | Experiments on language modeling and the downstream application of headline generation show the effectiveness of SDLM. |
Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have advanced mathematical reasoning, but they still struggle with out-of-distribution (OOD) issues. |
| Approach: | They propose a framework to evaluate the logical validity of reasoning steps . they retrieves semantically similar questions and steps for PRM as a warmup . |
| Outcome: | The proposed framework outperforms baseline models on multiple real-world datasets. |
Establishing Trustworthy LLM Evaluation via Shortcut Neuron Analysis (2025.acl-long)
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| Challenge: | Recent studies have focused on building dynamic benchmarks to address data contamination issues. |
| Approach: | They propose a method for identifying shortcut neurons through comparative and causal analysis to suppress shortcut neurons. |
| Outcome: | The proposed method overestimates contaminated models and is highly generalizable across benchmarks and hyperparameter settings. |
OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)
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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou
| Challenge: | a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say . |
| Approach: | They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible . |
| Outcome: | The proposed framework achieves state-of-the-art performance among open-source projects. |
Conjoin after Decompose: Improving Few-Shot Performance of Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing prompt-based NER models fail to detect entity boundaries, causing performance degradation. |
| Approach: | They propose a model which consists of a BART encoder and a parabiotic decoder and propose ' boundary expansion strategy' to enhance the model's capability in entity type classification. |
| Outcome: | The proposed model can achieve significant performance gains over state-of-the-art models. |
Target-based Sentiment Annotation in Chinese Financial News (2020.lrec-1)
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| Challenge: | Using a large corpus of 8,314 target-level sentiment annotations, sentiment classification on multiple opinion aspects/targets level is unsatisfactory. |
| Approach: | They propose to construct a large-scale target-based sentiment annotation corpus on Chinese financial news text. |
| Outcome: | The proposed corpus has 8,314 target-level sentiment annotations on Chinese financial news text. |
Large Language Models are Better Reasoners with Self-Verification (2023.findings-emnlp)
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| Challenge: | Existing methods to solve complex natural language processing tasks require multiple steps to verify the answers. |
| Approach: | They propose to use chain of thought prompting to solve reasoning tasks with large language models. |
| Outcome: | The proposed method can improve reasoning performance on arithmetic, commonsense, and logical reasoning datasets. |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction. |
| Approach: | They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack. |
| Outcome: | The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. |
BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis (2023.emnlp-industry)
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| Challenge: | Recent text-to-image models require multiple passes of prompt engineering by humans to produce satisfactory results for real-world applications. |
| Approach: | They propose a deep generative model to generate high-quality prompts from raw descriptions using visual feedback. |
| Outcome: | The proposed model produces high-quality prompts from simple raw descriptions . it can be integrated to a cloud-native AI platform to provide better image generation service in the cloud. |
Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training (2026.acl-long)
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Jihao Gu, Qihang Ai, Yingyao Wang, Pi Bu, Jingxuan Xing, Yue Cao, Zekun Zhu, Wei Jiang, Ziming Wang, Yingxiu Zhao, Ming-Liang Zhang, Jun Song, Yuning Jiang, Bo Zheng
| Challenge: | Existing approaches to training agents for visual-language models trap them in local optima, hindering exploration and error correction with the environment. |
| Approach: | They propose a hierarchical training recipe that bridges atomic action execution and strategic task completion. |
| Outcome: | The proposed training recipe bridges atomic action execution and strategic task completion. |