Papers by Wenzhi Wang

4 papers
Identification of Multiple Logical Interpretations in Counter-Arguments (2025.emnlp-main)

Copied to clipboard

Challenge: Counter-arguments (CAs) are a good way to improve learners' critical thinking skills . however, it is difficult to provide every learner tailored feedback due to limited human resources and heavy workloads.
Approach: They propose to annotate a dataset of 134 CAs annotated with 13 logical predicate questions and train a model with Reinforcement Learning with Verifiable Rewards to identify multiple logical interpretations.
Outcome: The proposed model performs on par with larger proprietary models.
Flee the Flaw: Annotating the Underlying Logic of Fallacious Arguments Through Templates and Slot-filling (2024.emnlp-main)

Copied to clipboard

Challenge: Prior work on quality assessment has focused on numerical scoring and fallacy type-labeling tasks, without aiming to analyze fallacy logic structures.
Approach: They propose four sets of explainable templates for common informal logical fallacies designed to explicate a fallacy’s implicit logic.
Outcome: The proposed models achieve a high agreement score and reasonable coverage 83% on 400 fallacious arguments and state-of-the-art language models struggle with detecting fallacy templates (0.47 accuracy).
Pre-training CLIP against Data Poisoning with Optimal Transport-based Matching and Alignment (2025.emnlp-main)

Copied to clipboard

Challenge: Recent studies have shown that Contrastive Language-Image Pre-training (CLIP) models are vulnerable to data poisoning and backdoor attacks due to massive training image-caption pairs crawled from the Internet.
Approach: They propose an Optimal Transport-based framework to reconstruct image-caption pairs and propose an optimal transport-based distance measure to re-assign new captions based on the proposed optimal transport distance.
Outcome: The proposed framework reduces the attack success rates of poisoning attacks to 0% in most cases.
Designing Logic Pattern Templates for Counter-Argument Logical Structure Analysis (2024.findings-emnlp)

Copied to clipboard

Challenge: Despite their effectiveness, the logical attack structure of counterarguments remains unexplored due to its complexity.
Approach: They propose a task to analyze logical attack structure of counterarguments in relation to their corresponding opponent argument using 10 new CA logic patterns.
Outcome: The proposed task achieves high annotator agreement and coverage and high coverage on a dataset of 778 CAs.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations