Papers by Yequan Wang
Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs (2026.acl-long)
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| Challenge: | Recent self-training approaches have reduced reliance on human-labeled data, which limits their scalability. |
| Approach: | They propose a team-based self-play algorithm that iteratively refines alignment without additional human supervision. |
| Outcome: | The proposed algorithm outperforms baselines and LLM benchmarks in the self-supervised setting. |
Multimodal Reasoning with Multimodal Knowledge Graph (2024.acl-long)
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| Challenge: | Multimodal reasoning with large language models (LLMs) often suffers from hallucinations and the presence of deficient or outdated knowledge within LLMs. |
| Approach: | They propose a multimodal reasoning method that leverages multimodal knowledge graphs to learn rich and semantic knowledge across modalities. |
| Outcome: | The proposed method outperforms state-of-the-art models on multimodal question answering and multimodal analogy reasoning tasks while training on only a small fraction of parameters. |
Reflection on Knowledge Graph for Large Language Models Reasoning (2025.findings-acl)
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Yigeng Zhou, Wu Li, Yifan Lu, Jing Li, Fangming Liu, Meishan Zhang, Yequan Wang, Daojing He, Honghai Liu, Min Zhang
| Challenge: | Existing methods for supplementing Large Language Models (LLMs) with knowledge graphs often introduce noise in the retrieval and reasoning pipeline, hindering their ability to integrate external knowledge for complex multi-hop question answering. |
| Approach: | They propose a framework to enhance LLMs' reasoning capabilities through reflective engagement with knowledge graphs by Query Decoupling, LLM-Driven Knowledge Graph Exploration, and Inference with Knowledge Reconstruction. |
| Outcome: | The proposed framework integrates external knowledge into LLMs and trains them to leverage this knowledge for answering questions. |
Rethinking Document-Level Relation Extraction: A Reality Check (2023.findings-acl)
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| Challenge: | Recent efforts push up performance boundaries of document-level relation extraction (DocRE) but these efforts are not promising. |
| Approach: | They construct four types of entity mention attacks to examine model robustness . they also have a close check on model usability in a more realistic setting . |
| Outcome: | The proposed model is based on a strong or untenable assumption in common . the model is robust under four types of mention attacks and usable in a realistic setting . |
CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction (2022.coling-1)
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| Challenge: | Existing solutions for quotation extraction use rule-based approaches and sequence labeling models. |
| Approach: | They propose a Context and Former-Label Enhanced Net for quotation extraction. |
| Outcome: | The proposed method achieves state-of-the-art performance on complicated quotation extraction on two public datasets and one proprietary dataset. |
If an LLM Were a Character, Would It Know Its Own Story? Evaluating Lifelong Learning in LLMs (2026.acl-long)
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Siqi Fan, Xiusheng Huang, Yiqun Yao, Xuezhi Fang, Kang Liu, Peng Han, Shuo Shang, Aixin Sun, Yequan Wang
| Challenge: | Existing benchmarks for large language models (LLMs) fail to capture these dynamics, focusing on static, open-ended evaluations. |
| Approach: | They propose a benchmark to assess lifelong learning in large language models . they use two episodic datasets rich in narrative structure and character interactions . |
| Outcome: | Experiments on LLMs show that non-parametric methods outperform parametric ones in managing stateful learning. |
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. |
Harmonizing the Past, Present, and Future: A Null-Space Constrained Region-Specific Method for Continual Learning in LLMs (2026.acl-long)
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Jinhui Chen, Shizhu He, Xingchang Yang, Huanxuan Liao, Yequan Wang, Xiangwen Liao, Wenhao Teng, Kang Liu, Jun Zhao
| Challenge: | Existing continual learning paradigms prioritize instant performance through dense updates, leading to catastrophic forgetting and rapid exhaustion of model capacity. |
| Approach: | They propose a method that preserves previously acquired knowledge and acquires new task-specific skills while preserving sufficient parameter capacity for subsequent adaptation. |
| Outcome: | The proposed method is based on the brain's functional partitioning and can be used to map tasks between specialized and generalist neurons. |
Break Through the Compression Bottleneck: From Theory to Practice (2026.findings-acl)
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| Challenge: | Existing compression methods suffer from bottleneck issues when compression ratio is increased. |
| Approach: | They propose a novel approach to combine low-rank decomposition and quantization methods to reduce the compression bottleneck. |
| Outcome: | The proposed method reduces the computational and memory overhead of existing methods while maintaining model accuracy. |
Efficient Prior-Guided Reasoning for Robust Retrieval-Augmented Generation under Conflicts (2026.acl-long)
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| Challenge: | Retrieval-Augmented Generation (RAG) has become a standard paradigm for grounding Large Language Models (LLMs) however, performance degrades substantially when faced with noisy, outdated, or conflicting retrieved information. |
| Approach: | They propose a framework that explicitly elicits the model’s parametric knowledge as prior information to guide reasoning on retrieved documents. |
| Outcome: | The proposed framework achieves robust performance across varying degrees of external inconsistency and noise. |
Theory-optimal Quantization Based on Flatness (2026.acl-long)
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| Challenge: | Recent approaches to quantization of Large Language Models (LLMs) have been widely adopted due to activation outliers, which degrade model performance especially at lower bit precision. |
| Approach: | They propose a new metric for quantization that strategically distributes outlier magnitudes across matrix dimensions via optimized diagonal operations. |
| Outcome: | The proposed framework achieves less than 1% accuracy drop in W4A4 quantization on the LLaMA-3-8B model and reduces the performance gap by 39.1% on the more challenging W2A4KV16 model. |
Position-Aware Depth Decay Decoding (D3): Boosting Large Language Model Inference Efficiency (2025.findings-acl)
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| Challenge: | Recent dynamic computation methods show that not all components are required for inference, enabling a training-free pipeline. |
| Approach: | They propose a token-position aware layer skipping framework to save 1.5x times operations efficiently while maintaining performance. |
| Outcome: | The proposed algorithm achieves 1.5x speedup on large language models with no retraining and with comparable performance on the GSM8K and BBH benchmarks. |
Improving Zero-shot LLM Re-Ranker with Risk Minimization (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are effective Query Likelihood Models, but their estimation is biased and the model's accuracy is poor. |
| Approach: | They propose a framework which leverages Bayesian decision theory to quantify and mitigate this bias. |
| Outcome: | The proposed framework improves re-ranking, especially in improving the Top-1 accuracy. |
Exploiting Contextual Knowledge in LLMs through 𝒱-usable Information based Layer Enhancement (2025.acl-long)
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| Challenge: | Existing approaches to enhance the context-faithfulness of Large Language Models (LLMs) ignore the fundamental mechanism of how contextual information is processed within LLMs’ internal states. |
| Approach: | They propose a method that enhances the utilization of contextual knowledge within LLMs’ internal representations by employing V-usable information analysis. |
| Outcome: | The proposed method improves context-faithfulness generation in Question-Answering tasks, particularly in scenarios involving unknown or conflicting contextual knowledge. |
Commonsense Knowledge Editing Based on Free-Text in LLMs (2024.emnlp-main)
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| Challenge: | Existing knowledge editing methods focus on editing triple-based facts such as entity-relation pairs and events (multiple triplets). |
| Approach: | They propose a Knowledge Localization for Free-Text method which uses a Dynamics-aware Module to locate the parameter positions corresponding to commonsense knowledge and a knowledge editing module to update knowledge. |
| Outcome: | The proposed method exploits the potential of the MLP and Attention layers and edits commonsense knowledge based on free-text. |
CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts (2022.findings-emnlp)
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| Challenge: | Comparative opinion classification is a common linguistic phenomenon. |
| Approach: | They propose a framework for comparative opinion classification using embedded knowledge in pre-trained language models. |
| Outcome: | The proposed framework delivers state-of-the-art and robust performance on all benchmark datasets. |
Multi-Modality Expansion and Retention for LLMs through Parameter Merging and Decoupling (2025.acl-long)
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Junlin Li, Guodong Du, Jing Li, Sim Kuan Goh, Wenya Wang, Yequan Wang, Fangming Liu, Ho-Kin Tang, Saleh Alharbi, Daojing He, Min Zhang
| Challenge: | Large Language Models (LLMs) are a cornerstone in artificial intelligence due to their exceptional performance. |
| Approach: | They propose a training-free approach that integrates existing MLLMs for effective multimodal expansion while retaining their original performance. |
| Outcome: | The proposed approach can expand LLMs' multimodal capabilities while retaining original performance. |
Discerning and Resolving Knowledge Conflicts through Adaptive Decoding with Contextual Information-Entropy Constraint (2024.findings-acl)
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| Challenge: | Existing decoding methods for large language models (LLMs) are specialized in resolving knowledge conflicts and could inadvertently deteriorate performance in absence of conflicts. |
| Approach: | They propose an adaptive decoding method to discern whether knowledge conflicts occur and resolve them by a contextual information-entropy constraint decoding technique. |
| Outcome: | The proposed method improves the model’s faithfulness to conflicting context and maintains high performance among non-conflicting contexts. |
EmotionTalk: An Interactive Chinese Multimodal Emotion Dataset With Rich Annotations (2026.findings-acl)
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Haoqin Sun, Jinghua Zhao, Xuechen Wang, Shiwan Zhao, Jiaming Zhou, Hui Wang, Xi Yang, Yequan Wang, Yonghua Lin
| Challenge: | Existing datasets face issues such as low quality, limited scale, and incomplete modalities, hindering model performance. |
| Approach: | They propose to use Chinese multimodal datasets to capture authentic emotional interplay from 19 professional actors. |
| Outcome: | The EmotionTalk dataset spans 23.6 hours of dyadic conversations across diverse scenarios. |
Knowledgeable Parameter Efficient Tuning Network for Commonsense Question Answering (2023.acl-long)
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| Challenge: | Existing commonsense question answering models incur prohibitive computation costs and poor interpretability . |
| Approach: | They propose a parameter efficient tuning network to pair PLMs with external knowledge for commonsense question answering. |
| Outcome: | The proposed adapter integrates entity- and query-related knowledge at a small cost. |
A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict (2022.findings-naacl)
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| Challenge: | Sarcasm employs ambivalence, where one says something positive but actually means negative . linguistically, it is difficult to recognize such sentiment conflict because the sentiments are mixed or even implicit . |
| Approach: | They propose a Dual-Channel Framework to model literal and implied sentiments separately . they propose sarcastic networks that can detect sarcasm sentiments in political debates . |
| Outcome: | The proposed framework achieves state-of-the-art on political debates and Twitter datasets. |
Spectral Disentanglement: Rank-Aware Task Adaptation for Rehearsal-free Continual Learning in LLMs (2026.acl-long)
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| Challenge: | Continual Learning (CL) for Large Language Models faces a fundamental Stability-Plasticity Dilemma . Rank-Blindness enforces a single rank constraint across diverse tasks, leading to catastrophic forgetting of earlier tasks and underfitting on complex new ones. |
| Approach: | They propose a rank-spectrum-based rehearsal-free framework that explicitly disentangles knowledge into two orthogonal subspaces. |
| Outcome: | The proposed framework achieves a superior stability-plasticity balance compared to single-rank baselines. |