Papers by Wenqing Zhou
Temporal Precision Matters: Brain-Tuning Speech Language Models with Millisecond-Resolution Neural Signals (2026.acl-long)
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| Challenge: | Language models have emerged as powerful tools for predicting human brain activity during language comprehension. |
| Approach: | They propose a technique that leverages electrocorticography’s millisecond precision to train speech language models. |
| Outcome: | The proposed technique improves brain alignment over pretrained and distillation models and produces higher gains in higher-order language regions. |
Conditional Dichotomy Quantification via Geometric Embedding (2025.acl-long)
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| Challenge: | Existing methods that rely on semantic similarity fail to capture the nuanced oppositional dynamics essential for these applications. |
| Approach: | They propose a task that formalizes the measurement of conditional dichotomy by using a dichotomian framework. |
| Outcome: | The proposed framework provides carefully constructed datasets covering debate, defeasible inference, and causal reasoning scenarios. |
Beyond Single-View Detection: A Dual-Space Reasoning Framework for Interpretable Harmful Meme Understanding (2026.acl-long)
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| Challenge: | Existing methods for identifying harmful memes rely on modal alignment or black-box classifiers . BPDMoE-Hate provides visual explanations for viewpoint selection and hierarchical structuring . |
| Approach: | They propose a framework that conceptualizes harmful meme detection as a process of "viewpoint decoupling and hierarchical fusion" they propose BPDMoE-Hate, which generates adversarial binary perspectives via VLMs and incorporates an adaptive viewpoint gating to facilitate viewpoint selection. |
| Outcome: | The proposed framework surpasses existing methods in performance and provides visual explanations for viewpoint selection and hierarchical structuring. |
Dynamic PMI-Guided Contrastive Decoding Reduces Hallucination in Large Language Models: A Unified Framework of Fine-Grained Input Transformations (2026.findings-acl)
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| Challenge: | Despite the remarkable generation capabilities of large language models, the issue of hallucination remains a critical challenge. |
| Approach: | They propose a contrastive decoding framework based on dynamic pointwise mutual information that disentangles spurious dependencies induced by context priors, lexical co-occurrences, and syntactic structures and prioritizes causal logic. |
| Outcome: | The proposed framework significantly improves the model’s factuality and reasoning robustness while maintaining high computational efficiency. |
Frame First, Then Extract: A Frame-Semantic Reasoning Pipeline for Zero-Shot Relation Triplet Extraction (2025.emnlp-main)
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| Challenge: | Existing methods to extract triplets for unseen relations rely on costly fine-tuning and lack structured semantic guidance. |
| Approach: | They propose a framework that adopts a "frame first, then extract" paradigm to extract triplets from unstructured text. |
| Outcome: | The proposed framework achieves competitive zero-shot performance on multiple benchmarks and can be used to enhance existing extraction methods. |