Papers by Changyue Wang
Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models (2024.findings-acl)
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| Challenge: | Existing studies on hallucination detection for LLMs focus on how to identify possible factrelated errors in outputs. |
| Approach: | They propose an unsupervised training framework that leverages the internal states of LLMs for real-time hallucination detection without requiring manual annotations. |
| Outcome: | The proposed framework outperforms existing state-of-the-art methods in hallucination detection. |
Decoupling Reasoning and Knowledge Injection for In-Context Knowledge Editing (2025.findings-acl)
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| Challenge: | Existing knowledge editing approaches directly edit model context without isolating target knowledge from the reasoning path of model inference, resulting in unreliable and low-quality outputs, especially in multi-hop tasks. |
| Approach: | They propose a framework that separates model reasoning from knowledge editing and propose 'DecKER' that allows users to modify specific factual associations without retraining the entire model. |
| Outcome: | The proposed framework significantly improves multi-hop reasoning performance by mitigating knowledge conflicts and preserving reasoning integrity. |
Knowledge Editing through Chain-of-Thought (2025.emnlp-main)
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| Challenge: | Existing knowledge editing methods focus on multi-hop QA tasks and require frequent retraining. |
| Approach: | They propose a new knowledge editing framework that updates large language models with new information to maintain their world knowledge without retraining. |
| Outcome: | The proposed method achieves state-of-the-art performance while offering superior generalization, effectiveness, and stability compared to existing methods. |