Interpretable Multimodal Misinformation Detection with Logic Reasoning (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for misinformation detection lack interpretability due to the black-box nature of the neural network. |
| Approach: | They propose a logic-based neural model which integrates interpretable logic clauses to express the reasoning process of the target task. |
| Outcome: | The proposed model can be generalizable across multiple misinformation sources and is based on three public datasets. |
Similar Papers
Do Not Guess, Verify: Logic-Guided Adaptive Reasoning for Multimodal Misinformation Detection (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing multimodal misinformation detection paradigms rely on passive aggregation of multimodal features and social signals. |
| Approach: | They propose a verification-oriented framework that integrates large vision–language models into multimodal misinformation detection through explicit rationale-guided reasoning. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on multimodal misinformation detection benchmarks while significantly reducing computational cost. |
Multimodal Misinformation Detection by Learning from Synthetic Data with Multimodal LLMs (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Obtaining large-scale, high-quality real-world fact-checking datasets is costly . generalizability of detectors trained on synthetic data to real-life scenarios remains unclear . |
| Approach: | They propose to use synthetic data to learn from real-world data to detect multimodal misinformation . they propose to combine model-agnostic data selection methods with real-life data distributions . |
| Outcome: | The proposed method improves the performance of a small MLLM on real-world fact-checking datasets, surpassing GPT-4V. |
From Detection to Understanding: Multi-Turn Reasoning for Video Misinformation Analysis (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks focus on binary veracity judgments and do not evaluate process-level justifications for misinformation models. |
| Approach: | They propose a video misinformation analysis benchmark that assesses reasoning in video misinterpretation. |
| Outcome: | The proposed framework improves reasoning accuracy and explanation quality compared to existing models . it covers 12 fine-grained deception categories and progresses from perceptual attribution to intent and persuasion analysis. |
Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing approaches to NLP are static and require manual formalization. |
| Approach: | They propose an adaptive, multi-paradigm, neuro-symbolic inference framework that automatically identifies formal reasoning strategies from problems expressed in natural language and dynamically selects and applies specialized formal logical solvers. |
| Outcome: | The proposed framework outperforms baselines on individual and multi-paradigm reasoning tasks by 17% and 6%. |
An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation (2022.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to interpret task-oriented dialogue systems employ an implicit reasoning strategy that makes the model predictions uninterpretable to humans. |
| Approach: | They propose a neuro-symbolic approach that performs explicit reasoning that justifies model decisions by reasoning chains. |
| Outcome: | The proposed approach achieves better results and introduces an interpretable decision process. |
Neural Multimodal Topic Modeling: A Comprehensive Evaluation (2024.lrec-main)
Copied to clipboard
| Challenge: | Neural topic models can find coherent and diverse topics in textual data, but they are limited in dealing with multimodal datasets. |
| Approach: | They propose two new topic modeling solutions and two new evaluation metrics for document multimodality. |
| Outcome: | The proposed models generate coherent and diverse topics on a rich dataset. |
Multimodal Pipeline for Collection of Misinformation Data from Telegram (2022.lrec-1)
Copied to clipboard
| Challenge: | a large portion of misinformation is spread via multimodal means, such as images and videos . a new pipeline for collecting misinformation from Telegram allows us to collect a greater variety of mis-information examples . |
| Approach: | They propose to use AI to understand misinformation flow across social media platforms . they collect data from Telegram groups which promote COVID-19 misinformation . |
| Outcome: | The proposed dataset contains almost one million messages from 2k different public channels related to spreading COVID-19 misinformation. |
Unveiling Fake News with Adversarial Arguments Generated by Multimodal Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for detecting fake news rely on neural networks to learn latent feature representations with limited real-world understanding. |
| Approach: | They propose a method that leverages Multimodal Large Language Models for fake news detection that introduces adversarial reasoning through debates from opposing perspectives. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on four fake news detection datasets. |
Multimodal Logical Inference System for Visual-Textual Entailment (P19-2)
Copied to clipboard
| Challenge: | Recent studies of multimodal inference provide challenging tasks such as visual question answering and visual reasoning. |
| Approach: | They propose an unsupervised multimodal logical inference system that can prove entailment relations between texts and images by combing semantic parsing and theorem proving. |
| Outcome: | The proposed system can handle semantically complex sentences for visual-textual inference. |
ACQUIRED: A Dataset for Answering Counterfactual Questions In Real-Life Videos (2023.emnlp-main)
Copied to clipboard
Te-Lin Wu, Zi-Yi Dou, Qingyuan Hu, Yu Hou, Nischal Chandra, Marjorie Freedman, Ralph Weischedel, Nanyun Peng
| Challenge: | despite its importance, there are few datasets that cover multimodal counterfactual reasoning . a dataset focusing on this area is limited because of its limited coverage over synthetic environments . |
| Approach: | They develop a video question answering dataset that provides questions on multimodal reasoning . they ask questions about counterfactual hypotheses over visual events . |
| Outcome: | The proposed dataset shows a significant performance gap between models and humans . it provides questions that span physical, social, and temporal dimensions . |