Papers by Zhongyuan Wang
CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language Models (2024.findings-emnlp)
Copied to clipboard
Yaojia Lv, Haojie Pan, Zekun Wang, Jiafeng Liang, Yuanxing Liu, Ruiji Fu, Ming Liu, Zhongyuan Wang, Bing Qin
| Challenge: | Recent advances in large language models (LLMs) focus on replicating human cognition in specific contexts, overlooking the inherently dynamic nature of cognition. |
| Approach: | They propose a task to assess cognitive dynamics of large language models (LLMs) they introduce a benchmark and two evaluation metrics to validate the benchmark and evaluate it through participant surveys. |
| Outcome: | The proposed task overcomes the limitations of existing methods and is available for download. |
Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension (2020.coling-main)
Copied to clipboard
| Challenge: | Existing approaches to filter noise for machine reading comprehension (MRC) are difficult to control and introduce noisy data. |
| Approach: | They propose a hierarchical loss correction strategy to avoid fitting noise and enhance clean supervision signals by using an unsupervisedly fitted Gaussian mixture model and a hard bootstrapping loss method. |
| Outcome: | The proposed methods can help improve models significantly on weakly supervised machine reading comprehension datasets. |
SCALER: Synthetic Scalable Adaptive Learning Environment for Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Reinforcement learning (RL) is a principled way to enhance the reasoning capabilities of large language models, yet its effectiveness hinges on training signals that remain informative as models evolve. |
| Approach: | They propose a framework that sustains effective learning signals through adaptive environment design that transforms real-world programming problems into verifiable reasoning environments with controllable difficulty and unbounded instance generation. |
| Outcome: | The proposed framework outperforms baselines across diverse reasoning benchmarks and exhibits more stable, long-horizon training dynamics. |
Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to disfluency detection rely on human annotations, which are expensive to obtain. |
| Approach: | They propose an unsupervised learning paradigm which can work with unlabeled text corpora. |
| Outcome: | The proposed method performs better than existing supervised systems using word embeddings. |
Syntactic Graph Convolutional Network for Spoken Language Understanding (2020.coling-main)
Copied to clipboard
| Challenge: | Existing work on slot filling and intent detection builds joint models without prior knowledge of linguistic knowledge. |
| Approach: | They propose a joint model that integrates syntactic structure for learning slot filling and intent detection jointly. |
| Outcome: | The proposed model outperforms existing models on two public benchmark datasets and further improves on slot filling and intent detection. |
Just Ask One More Time! Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios (2024.findings-acl)
Copied to clipboard
Lei Lin, Jiayi Fu, Pengli Liu, Qingyang Li, Yan Gong, Junchen Wan, Fuzheng Zhang, Zhongyuan Wang, Di Zhang, Kun Gai
| Challenge: | chain-of-thought (CoT) prompting has been shown to be effective on complex reasoning tasks, but the naive greedy decoding used in CoT prompting causes the repetitiveness and local optimality. |
| Approach: | They propose a generalizable ensemble-optimization method that uses a set of reasoning paths to prompt a language model one more time to determine the optimal answer. |
| Outcome: | The proposed method can be generalized to almost all scenarios where the type of input questions and answer format of reasoning paths may be unknown. |
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding (2022.coling-1)
Copied to clipboard
| Challenge: | a new method for learning unsupervised sentence embeddings is proposed . unsup-SimCSE is biased because of the length information encoded into the sentence embeds . |
| Approach: | They propose a new unsupervised sentence embedding method that uses dropout to obtain positive pairs from a pre-trained Transformer encoder. |
| Outcome: | The proposed method outperforms the state-of-the-art unsup-SimCSE on a STS task. |
MapNav: A Novel Memory Representation via Annotated Semantic Maps for VLM-based Vision-and-Language Navigation (2025.acl-long)
Copied to clipboard
Lingfeng Zhang, Xiaoshuai Hao, Qinwen Xu, Qiang Zhang, Xinyao Zhang, Pengwei Wang, Jing Zhang, Zhongyuan Wang, Shanghang Zhang, Renjing Xu
| Challenge: | Vision-language navigation (VLN) is a key task in Embodied AI . traditional approaches rely on historical observations as spatio-temporal contexts for decision making . |
| Approach: | They propose a vision-language navigation model that leverages an annotation system to replace historical frames. |
| Outcome: | The proposed model can be used as a new memory representation method in vision-language navigation . it can be applied to simulated and real-world environments, and it is validated by experiments . |
RaP: Redundancy-aware Video-language Pre-training for Text-Video Retrieval (2022.findings-emnlp)
Copied to clipboard
| Challenge: | sparse sampling of videos suffers from inter-modal redundancy and visual redundancies . et al., 2021) proposes to sparsestly sample frames from videos to alleviate temporal redundance . |
| Approach: | They propose to use sparse sampling to alleviate temporal redundancy in videos . they propose to penalize high-redundant video patches and text tokens . |
| Outcome: | The proposed method improves on four benchmark datasets. |
SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis (2025.findings-emnlp)
Copied to clipboard
Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, Fei Bai, Jia Deng, Xin Zhao, Zheng Liu, Lei Fang, Zhongyuan Wang, Ji-Rong Wen
| Challenge: | Existing approaches to deep search training lack high-quality training trajectories, prohibitive computational costs and lack of high-fidelity training data. |
| Approach: | They propose a framework that synthesizes high-quality training data by simulating real user interactions in live web search environments. |
| Outcome: | The proposed framework synthesizes high-quality training data by simulating user interactions in live web search environments. |
Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMs (2024.lrec-main)
Copied to clipboard
Chenxi Sun, Hongzhi Zhang, Zijia Lin, Jingyuan Zhang, Fuzheng Zhang, Zhongyuan Wang, Bin Chen, Chengru Song, Di Zhang, Kun Gai, Deyi Xiong
| Challenge: | Large language models have demonstrated exceptional capability in natural language understanding and generation, but their generation speed is limited by the inherently sequential nature of their decoding process. |
| Approach: | They propose a method that accelerates decoding process without sacrificing quality . they propose lexical unit decoding, which can be integrated with other methods . |
| Outcome: | The proposed method significantly reduces decoding time while maintaining quality while maintaining output quality. |
Smoothed Contrastive Learning for Unsupervised Sentence Embedding (2022.coling-1)
Copied to clipboard
| Challenge: | Unsupervised contrastive sentence embedding models use InfoNCE loss function . increasing batch size leads to performance degradation when it exceeds threshold . |
| Approach: | They propose a simple smoothing strategy upon the InfoNCE loss function to reduce the number of false-negative pairs in a batch without increasing the batch size. |
| Outcome: | The proposed smoothing strategy improves unsupervised SimCSE on semantic similarity tasks. |
Decompose, Prioritize, and Eliminate: Dynamically Integrating Diverse Representations for Multimodal Named Entity Recognition (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing research on multi-modal Named Entity Recognition (MNER) does not integrate all multi-modal representations to provide rich contextual information to improve NER. |
| Approach: | They propose an iterative reasoning framework that integrates all the diverse multi-modal representations following the strategy of "decompose, prioritize, and eliminate" . they propose to use hierarchically connected fusion layers to prioritize transitions from "easy-to-hard" and "coarse-to fine" |
| Outcome: | The proposed framework integrates all the diverse multi-modal representations following the strategy of "decompose, prioritize, and eliminate". |
Adaptive Unsupervised Self-training for Disfluency Detection (2022.coling-1)
Copied to clipboard
| Challenge: | Recent studies on disfluency detection heavily relies on human annotations, which are difficult and expensive to obtain in practice. |
| Approach: | They propose an unsupervised method that reweights the importance of each training example according to its grammatical feature and prediction confidence. |
| Outcome: | The proposed method improves 2.3 points over the current SOTA unsupervised method and is competitive with the SOTA supervised method. |
NavA3: Understanding Any Instruction, Navigating Anywhere, Finding Anything (2026.acl-long)
Copied to clipboard
Lingfeng Zhang, Xiaoshuai Hao, Yingbo Tang, Haoxiang Fu, Xinyu Zheng, Pengwei Wang, Zhongyuan Wang, Wenbo Ding, Shanghang Zhang
| Challenge: | Existing embodied navigation methods struggle with such tasks due to their limitations in comprehending high-level human instructions and localizing objects with an open vocabulary. |
| Approach: | They propose a hierarchical framework for long-horizon navigation that integrates human instructions with 3D scene views. |
| Outcome: | The proposed model achieves SOTA results and can complete long-horizon navigation tasks across different robot embodiments in real-world environments. |
InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies on contrastive learning for sentence embeddings are weak . researchers have started to use contrastive training to learn better unsupervised sentences. |
| Approach: | They propose an information-aggregated contrastive learning framework for learning unsupervised sentence embeddings. |
| Outcome: | The proposed framework outperforms SimCSE on several benchmark datasets w.r.t the semantic text similarity task. |
Table Fact Verification with Structure-Aware Transformer (2020.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained models cannot be used to encode semi-structured data because of their nature. |
| Approach: | They propose a Structure-Aware Transformer which injects table structural information into mask . method could combine symbolic and linguistic reasoning, they propose . |
| Outcome: | The proposed method outperforms baseline on a large scale table verification dataset. |