Papers by Aiping Xiong
Beyond Evidence: Belief-Chain Conditioning for Persuasive Misinformation Debunking Explanation (2026.findings-acl)
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| Challenge: | Existing methods to misinformation correction focus on relying on audience beliefs to generate factually accurate responses and to engage with users' mental states. |
| Approach: | They construct large language models with cognitive chains and use them to model their outputs on beliefs that engage with users' mental states. |
| Outcome: | The proposed model improves explanation quality for audiences with misinformation-aligned beliefs by incorporating believers’ chains into the model. |
LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis (2023.findings-emnlp)
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| Challenge: | Recent research shows that large language models can replicate human-like behavior in various tasks. |
| Approach: | They propose a framework for human-LLM collaboration to conduct TA with in-context learning (ICL) they propose to use survey data to frame discussions with an LLM to generate a final codebook for TA. |
| Outcome: | The proposed framework outperforms crowd workers on text-annotation tasks and yields similar coding quality to that of human coders but reduces TA’s labor and time demands. |
Is Explanation the Cure? Misinformation Mitigation in the Short Term and Long Term (2023.findings-emnlp)
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| Challenge: | Using natural language processing (NLP), there is an ongoing shift towards NLPbased solutions such as fake news detection and generation of fact-checked, counterfactual explanations. |
| Approach: | They compare the effectiveness of a warning label and state-of-the-art counterfactual explanations generated by natural language generation (GPT4) models in debunking misinformation. |
| Outcome: | The proposed explanations significantly decrease participants’ self-reported belief in fake claims for the short-term and long-term. |
Enhancing Perception: Refining Explanations of News Claims with LLM Conversations (2024.findings-naacl)
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| Challenge: | a new framework for Large Language Models (LLMs) streamlines the task of crafting explanations for fake news . a study compared refinement conversations between human and LLMs to enhance the effectiveness of LLM explanations . |
| Approach: | They propose a framework for Large Language Models to streamline the task of crafting fake news explanations. |
| Outcome: | The proposed framework enhances the process of crafting explanations for fake news claims through conversational refinement. |