Papers by Yujie Wang
Contrastive Video-Language Learning with Fine-grained Frame Sampling (2022.aacl-main)
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| Challenge: | despite recent progress in video and language representation learning, the weak or sparse correspondence between the two modalities remains a bottleneck. |
| Approach: | They propose a fine-grained contrastive objective for video frame sampling to improve cross-modal correspondence. |
| Outcome: | The proposed approach achieves state-of-the-art performance on YouCookII with long videos. |
HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST (2026.findings-acl)
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| Challenge: | Existing approaches to zero-shot Dialog State Tracking (zs-DST) are inadequate to generalize to new domains without extensive training. |
| Approach: | They propose a framework that enhances zero-shot slot inference through robust prompt alignment. |
| Outcome: | Experiments on multi-domain datasets show that HiCoLoRA outperforms baselines, achieving SOTA in zs-DST. |
What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations (2026.acl-short)
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Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu, Ningyu Zhang, Lanning Wei, Lun Du, Da Zheng, Huajun Chen
| Challenge: | Existing approaches to replicate AI research are limited by insufficient background knowledge and the limitations of retrieval-augmented generation methods. |
| Approach: | They propose a pluggable, paper-centric knowledge base that integrates code snippets and technical insights extracted from scientific literature into a verifiable, executable representation. |
| Outcome: | The proposed knowledge base shows significant performance gains on paperBench when integrated into three agent frameworks with two different LLMs. |
SMART: Evaluating LLMs’ Mathematical Reasoning via a Human Cognitive Process-Inspired Benchmark (2026.acl-long)
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| Challenge: | Existing evaluation methods focus on the final answer or on the intermediate reasoning steps, overlooking its inherently multi-stage and multi-dimensional nature. |
| Approach: | They propose a benchmark that decomposes mathematical problem-solving into four cognitive dimensions and introduces dimension-specific tasks to measure their cognitive processes. |
| Outcome: | The proposed model decomposes mathematical problem-solving into four cognitive dimensions and introduces dimension-specific tasks to measure their cognitive processes. |
FOREVER: Forgetting Curve-Inspired Memory Replay for Language Model Continual Learning (2026.acl-long)
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Yujie Feng, Hao Wang, Jian Li, Xu Chu, Zhaolu Kang, Yiran Liu, Yasha Wang, Philip S. Yu, Xiao-Ming Wu
| Challenge: | Continual learning (CL) for large language models (LLMs) aims to enable sequential knowledge acquisition without catastrophic forgetting. |
| Approach: | They propose a framework that aligns replay schedules with a model-centric notion of time. |
| Outcome: | Experiments on three benchmarks show that FOREVER consistently mitigates catastrophic forgetting. |
Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis (2022.findings-emnlp)
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| Challenge: | Existing learning metrics are limited to tasks where large human ratings are available. |
| Approach: | They propose a model-based natural language generation (NLG) evaluation metric that is highly correlated with human judgements without requiring human annotation. |
| Outcome: | The proposed metric outperforms all prior unsupervised metrics on multiple NLG tasks including translation, image captioning, and WebNLG text generation. |
ULN: Towards Underspecified Vision-and-Language Navigation (2022.emnlp-main)
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| Challenge: | Existing vision-and-language navigation models are brittle to multi-level language underspecification. |
| Approach: | They propose to use multi-level underspecified instructions to guide agents . they propose to learn GSS for navigation agent to ground multi- level instructions . experimental results show existing VLN models are still brittle to multi-language underspecification . |
| Outcome: | Experimental results show that the proposed framework outperforms baselines on ULN by 10% relative success rate across all levels. |
Let’s Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought (2023.emnlp-main)
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Vaishnavi Himakunthala, Andy Ouyang, Daniel Rose, Ryan He, Alex Mei, Yujie Lu, Chinmay Sonar, Michael Saxon, William Wang
| Challenge: | Existing studies show vision-language systems can reason about images using natural language, but their capacity for video reasoning remains underexplored. |
| Approach: | They propose to frame video reasoning as the sequential understanding of a small number of keyframes, thereby leveraging the power and robustness of vision-language systems' capacity to reason about images using natural language. |
| Outcome: | The proposed models can generate multiple intermediate keyframes and predict future keyframe, and they perform poorly on GPT-4, GPT-3, and VICUNA. |
DocSplit: Simple Contrastive Pretraining for Large Document Embeddings (2023.findings-emnlp)
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| Challenge: | Existing model pretraining methods only consider local information, resulting in low-quality embeddings for large documents. |
| Approach: | They propose a new method which forces models to consider the entire global context of a large document. |
| Outcome: | The proposed method outperforms existing models on document classification, few shot learning, and retrieval tasks. |
Stability Implies Redundancy: Delta Attention Selective Halting for Efficient Long-Context Prefilling (2026.acl-long)
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| Challenge: | Existing methods to reduce sequence length rely on heuristics that break compatibility with hardware-efficient kernels like FlashAttention. |
| Approach: | They propose a method that selectively halts stabilized tokens by monitoring layer-wise update dynamics of the self-attention mechanism. |
| Outcome: | The proposed method can reduce prefill complexity while preserving model accuracy and hardware efficiency. |
PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations (2026.acl-long)
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Yuhe Wu, Guangyu Wang, Yuran Chen, Jiatong Zhang, Yutong Zhang, Yujie Chen, Jiaming Shang, Guang Zhang, Zhuang Liu
| Challenge: | Existing benchmarks for hallucination evaluation rely on mixed queries and posterior evaluation, which quantifies hallucinosity severity but offers limited insight into where and why they occur. |
| Approach: | They propose a controlled benchmark that disentangles hallucinations into four dimensions: knowledge missing, knowledge errors, reasoning errors, and instruction-following errors. |
| Outcome: | The proposed model disentangles hallucinations into four dimensions: knowledge missing, knowledge errors, reasoning errors, and instruction-following errors. |
ChildMandarin: A Comprehensive Mandarin Speech Dataset for Young Children Aged 3-5 (2025.acl-long)
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Jiaming Zhou, Shiyao Wang, Shiwan Zhao, Jiabei He, Haoqin Sun, Hui Wang, Cheng Liu, Aobo Kong, Yujie Guo, Xi Yang, Yequan Wang, Yonghua Lin, Yong Qin
| Challenge: | Automatic speech recognition systems have advanced significantly with models like Whisper, Conformer, and self-supervised frameworks such as Wav2vec 2.0. |
| Approach: | They propose to use Mandarin speech datasets to analyze pronunciation and tone of children aged 3 to 5 and evaluate their models on speaker verification (SV) They find that the datasets are more robust than those used by adult speech recognition systems and are open-source and available for all academic purposes. |
| Outcome: | The proposed dataset includes 41.25 hours of speech with carefully crafted manual transcriptions, collected from 397 speakers across various provinces in China, with balanced gender representation. |
DarwinTOD: LLM-Driven Lifelong Self-evolution for Task-oriented Dialog Systems (2026.acl-long)
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| Challenge: | Continual learning approaches fail to achieve autonomy lifelong improvement in dynamic environments . current task-oriented dialog systems are static, unable to learn from ongoing interactions . |
| Approach: | They propose a lifelong self-evolving dialog framework that integrates evolutionary computation and LLM driven self-improvement into a single framework. |
| Outcome: | The proposed framework surpasses state-of-the-art methods and exhibits continuous performance gains throughout evolution. |
Hierarchical Enhancement Framework for Aspect-based Argument Mining (2023.findings-emnlp)
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| Challenge: | Existing methods have primarily treated ABAM as a nested named entity recognition problem, overlooking the need for tailored strategies to effectively address the specific challenges of ABA M tasks. |
| Approach: | They propose a layer-based Hierarchical Enhancement Framework (HEF) for Aspect-Based Argument Mining and introduce three new components to improve the performance and accuracy. |
| Outcome: | Experiments on multiple datasets and tasks verify the effectiveness of the proposed framework and components. |
Enhancing Event Causality Identification with LLM Knowledge and Concept-Level Event Relations (2025.coling-main)
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| Challenge: | Existing methods to identify causal relationships between events often overlook the dependencies between similar events. |
| Approach: | They propose an ECI method enhanced by LLM Knowledge and Concept-Level Event Relations (LKCER) the method constructs a conceptual-level heterogeneous event graph by leveraging local contextual information of related event mentions. |
| Outcome: | The proposed method outperforms previous state-of-the-art methods on both benchmarks, EventStoryLine and Causal-TimeBank. |
Recurrent Knowledge Identification and Fusion for Language Model Continual Learning (2025.acl-long)
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Yujie Feng, Xujia Wang, Zexin Lu, Shenghong Fu, Guangyuan Shi, Yongxin Xu, Yasha Wang, Philip S. Yu, Xu Chu, Xiao-Ming Wu
| Challenge: | Continual learning (CL) is crucial for large language models without costly retraining. |
| Approach: | They propose a framework for recurrent knowledge identification and fusion that enables dynamic estimation of parameter importance distributions to enhance knowledge transfer. |
| Outcome: | The proposed framework mitigates catastrophic forgetting and enhances knowledge transfer. |
BMAM: Brain-inspired Multi-Agent Memory Framework (2026.findings-acl)
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| Challenge: | Language-model-based agents operating over extended interactions face persistent challenges in preserving temporally grounded information and maintaining behavioral consistency across sessions. |
| Approach: | They propose a general-purpose memory architecture that decomposes agent memory into six components that operate at complementary time scales. |
| Outcome: | BMAM outperforms memory-augmented baselines on LoCoMo benchmarks with 78.45% accuracy . a targeted refinement of the temporal-trigger heuristics raises LongMemEval multi-session accuracy from 45.2% to 56.4%. |
Cross-Document Cross-Lingual NLI via RST-Enhanced Graph Fusion and Interpretability Prediction (2025.emnlp-main)
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| Challenge: | Despite the development of many subdirections, Cross-Document Cross-Lingual NLI remains largely unexplored. |
| Approach: | They propose a novel paradigm that extends traditional NLI capabilities to multi-document, multilingual scenarios by integrating RST-enhanced graph fusion with interpretability-aware prediction. |
| Outcome: | The proposed method improves on existing models and document-level NLI to multi-document, multilingual scenarios. |
Evaluating Cognitive-Behavioral Fixation via Multimodal User Viewing Patterns on Social Media (2025.emnlp-main)
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| Challenge: | Digital media platforms often contribute to cognitive-behavioral fixation, a phenomenon in which users exhibit sustained and repetitive engagement with narrow content domains. |
| Approach: | They propose a multimodal topic extraction module and a cognitive-behavioral fixation quantification module that collaboratively enable adaptive, hierarchical, and interpretable assessment of user behavior. |
| Outcome: | The proposed framework lays the groundwork for scalable computational analysis of cognitive fixation. |
ChildTalk: A Multi-Dialect Chinese Child Speech Corpus with Full-Length Child–Caregiver Conversations for Speech Recognition (2026.findings-acl)
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| Challenge: | Automatic speech recognition (ASR) for children remains challenging due to developmental variability and the scarcity of high-quality corpora. |
| Approach: | They propose a large-scale Chinese child speech corpus that contains 112.5 hours of speech from 498 children and 500 caregivers. |
| Outcome: | The proposed model improves in-domain and cross-domain performance on children's speech. |
Multimodal Procedural Planning via Dual Text-Image Prompting (2024.findings-emnlp)
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| Challenge: | Embodied agents have demonstrated performance in following instructions informed by texts and images . however, the potential of models providing useful guidelines for humans to complete tasks remains underexplored . |
| Approach: | They propose a multimodal procedural planning task that generates paired text-image plans . this task provides more complementary and informative guidance than unimodal plans a . authors propose modality prompting methods that leverage zero-shot reasoning ability . |
| Outcome: | The proposed method improves the interaction in dual modalities and provides more information than unimodal plans. |
Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning (2025.acl-long)
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Yongxin Xu, Ruizhe Zhang, Xinke Jiang, Yujie Feng, Yuzhen Xiao, Xinyu Ma, Runchuan Zhu, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Existing methods for integrating internal and external knowledge lack effective control mechanisms for generating hallucinations and dealing with outdated knowledge. |
| Approach: | They propose a framework that decouples, identifies, and purposefully optimizes parameter subspaces related to adherence and robustness. |
| Outcome: | The proposed framework decouples, identifies, and purposefully optimizes parameter subspaces related to adherence and robustness. |
Imagination-Augmented Natural Language Understanding (2022.naacl-main)
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| Challenge: | Existing methods for Natural Language Understanding focus on textual signals, which hinders models from learning efficiently from limited data samples. |
| Approach: | They propose an Imagination-Augmented Cross-modal Encoder to solve natural language understanding tasks from a novel learning perspective. |
| Outcome: | The proposed learning paradigm bridges the gap between human and agent language understanding in both linguistic and perceptual procedures. |
Sparse Brains are Also Adaptive Brains: Cognitive-Load-Aware Dynamic Activation for LLMs (2026.findings-eacl)
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| Challenge: | Existing sparsity methods lack adaptivity to contextual or model structural demands or incur prohibitive computational overhead. |
| Approach: | They propose a Cognitive-Load-Aware Dynamic Activation framework that synergizes statistical sparsity with semantic adaptability. |
| Outcome: | The proposed framework achieves 20% average speedup with less than 2% accuracy degradation outperforming Griffin and TT. |
CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation (2024.lrec-main)
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| Challenge: | Metaphors are a prominent linguistic device in human language and literature, as they add color, imagery, and emphasis to enhance effective communication. |
| Approach: | They propose a large-scale high quality annotated Chinese Metaphor Corpus . they use a set of guidelines to ensure the accuracy and consistency of their annotations . |
| Outcome: | The proposed corpus generates metaphors that resonate more with real-world intuition. |
DyBBT: Dynamic Balance via Bandit-inspired Targeting for Dialog Policy with Cognitive Dual Systems (2026.acl-long)
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| Challenge: | Task oriented dialog systems often rely on static exploration strategies that do not adapt to dynamic dialog contexts. |
| Approach: | They propose a dialog policy learning framework that formalizes the exploration challenge through a structured cognitive state space C. |
| Outcome: | The proposed framework achieves SOTA performance in success rate, efficiency, and generalization. |
Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting (2023.findings-emnlp)
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| Challenge: | Existing systems for mental health support are shallow and heuristic, e.g., analyzing emotions and generating comforting responses. |
| Approach: | They propose to use cognitive distortion detection to perform diagnosis on the patient’s speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts and schema analysis to summarize the cognition schemas. |
| Outcome: | The proposed system improves on ChatGPT for cognitive distortion detection while generating high-quality rationales approved by human experts. |
When Phrases Meet Probabilities: Enabling Open Relation Extraction with Cooperating Large Language Models (2024.acl-long)
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| Challenge: | Existing clustering-based open relation extraction methods use pre-trained language models . embeddings from language models are high-dimensional and anisotropic, so there is a gap . |
| Approach: | They propose a framework that makes two LLMs work collaboratively to achieve clustering. |
| Outcome: | The proposed framework outperforms existing methods by 1.4%3.13% on different datasets. |
Collaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation (2023.emnlp-main)
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| Challenge: | Experimental results show that GPT-k models focus more on inserting modifiers than predicting spontaneous changes in the primary subject matter. |
| Approach: | They compare the common edits made by humans and GPT-k models to examine their performance in prompting T2I. |
| Outcome: | The proposed models improve the prompt editing process by 20-30%, the authors show . they show that humans tend to replace words and phrases with modifiers . |
Dynamic Heterogeneous-Graph Reasoning with Language Models and Knowledge Representation Learning for Commonsense Question Answering (2023.acl-long)
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| Challenge: | Existing methods for QA use knowledge graphs, but they ignore subgraph optimization and subgraph deepening. |
| Approach: | They propose a dynamic heterogeneous-graph reasoning method with LMs and knowledge representation learning that optimizes the structure and knowledge representing of the HKG using a two-stage pruning strategy and knowledge-representation learning. |
| Outcome: | The proposed method improves on existing methods at CommonsenseQA and OpenBookQA. |
DenseSSM: State Space Models with Dense Hidden Connection for Efficient Large Language Models (2025.naacl-long)
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| Challenge: | Large language models (LLMs) face excessive computational and memory requirements due to the commonly used Transformer architecture. |
| Approach: | They propose a method to enhance the flow of hidden information between layers in large language models by selectively integrating shallow-layer hidden states into deeper layers. |
| Outcome: | The proposed method maintains parallelizability and inference efficiency of SSMs while significantly boosting performance on public benchmarks. |
AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images (2026.acl-long)
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Bo Zhang, Tzu-Yen Ma, Zichen Tang, Junpeng Ding, Zirui Wang, Yizhuo Zhao, Peilin Gao, Zijie Xi, Zixin Ding, Haiyang Sun, Haocheng Gao, Yuan Liu, Liangjia Wang, Yiling Huang, Yujie Wang, Yuyue Zhang, Ronghui Xi, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Haihong E
| Challenge: | AEGIS examines whether current models can effectively audit AI-generated images in academic papers. |
| Approach: | They propose a holistic benchmark for forensic analysis of AI-Generated academic ImageS that reveals limitations in academic image forensics. |
| Outcome: | AEGIS compared with existing benchmarks on seven academic categories and features key advances in forensic analysis. |
Visualize Before You Write: Imagination-Guided Open-Ended Text Generation (2023.findings-eacl)
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| Challenge: | Existing tools for text-to-image synthesis can visualize machine imaginations for a given context. |
| Approach: | They propose a framework that uses machine-generated images to guide language models in open-ended text generation. |
| Outcome: | The proposed framework is effective on open-ended text generation tasks while showing minor degeneration. |
HCRE: LLM-based Hierarchical Classification for Cross-Document Relation Extraction with a Prediction-then-Verification Strategy (2026.findings-acl)
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| Challenge: | Existing approaches to cross-document relation extraction (RE) focus on identifying relations between head and tail entities from single sentence or document. |
| Approach: | They propose a hierarchical relation tree-based LLM-based hierarchic classification model for cross-document relation extraction (HCRE) based on predefined relations, the model can perform hierarchically classification level by level. |
| Outcome: | The proposed model outperforms existing baselines and validates its effectiveness. |
GeoEdit: Geometric Knowledge Editing for Large Language Models (2025.emnlp-main)
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Yujie Feng, Li-Ming Zhan, Zexin Lu, Yongxin Xu, Xu Chu, Yasha Wang, Jiannong Cao, Philip S. Yu, Xiao-Ming Wu
| Challenge: | Existing training-based model editing methods struggle to incorporate new knowledge while preserving unrelated general knowledge. |
| Approach: | They propose a framework that uses geometric relationships to differentiate between neurons associated with new knowledge updates and those related to general knowledge perturbations. |
| Outcome: | The proposed framework avoids updating neurons with directions approximately orthogonal to existing knowledge, thus preserving the model’s generalization ability. |
MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering (2022.emnlp-main)
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| Challenge: | Existing methods for open relation extraction (OpenRE) focus on labeled and pre-defined instances, which are costly to acquire in reality. |
| Approach: | They propose a framework that can extract relations without pre-defined types from open-domain corpus with efficient knowledge transfer from a few pre-determined relational instances. |
| Outcome: | The proposed framework achieves the new SOTA results for OpenRE on different datasets. |
AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning (2025.emnlp-main)
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Yujie Feng, Jian Li, Xiaoyu Dong, Pengfei Xu, Xiaohui Zhou, Yujia Zhang, Zexin Lu, Yasha Wang, Alan Zhao, Xu Chu, Xiao-Ming Wu
| Challenge: | Recent model merging-based methods struggle to effectively manage the trade-off between learning new knowledge and preventing catastrophic forgetting. |
| Approach: | They propose a model merging framework that utilizes learning and forgetting signals from the training trajectory to dynamically monitor the model’s training status. |
| Outcome: | The proposed framework achieves significant performance improvements over existing state-of-the-art methods on three CL benchmarks with various model sizes (from 770M to 13B). |
Investigating Inference-time Scaling for Chain of Multi-modal Thought: A Preliminary Study (2025.findings-acl)
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| Challenge: | Inference-time scaling of chain-of-thought (CoT) has been demonstrated as a promising approach for addressing multi-modal reasoning tasks. |
| Approach: | They propose to integrate visual and textual modalities within the reasoning process . they adopt a consistency-enhanced verifier to ensure effective guidance for both methods across different thought paradigms. |
| Outcome: | The proposed method outperforms text-only reasoning on 10 tasks spanning diverse domains and requires higher token consumption for processing richer visual inputs. |
CTAP for Chinese:A Linguistic Complexity Feature Automatic Calculation Platform (2022.lrec-1)
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| Challenge: | Existing tools to analyze linguistic complexity are limited and different because of different research purposes. |
| Approach: | They propose to integrate Chinese component into CTAP to analyze linguistic complexity . they propose to use 196 linguistic complex indexes to calculate linguistic characteristics . |
| Outcome: | The proposed indexes are compared with three linguistic complexity tools for Chinese . the proposed index sets include four levels of 196 linguistic complex indexe . |