Papers by Jianhua Zhao
ReFL: Reflective Feedback Learning for Hallucination Detection of Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for detecting hallucinations depend on external knowledge sources, incurring high computational costs and limiting real-time applicability, or extract the model’s internal states, leading to poor generalization. |
| Approach: | They propose a hallucination detection framework that leverages corrective in-context learning to guide LLMs to recognize their own prediction errors and adjust internal representations, critically without updating model weights. |
| Outcome: | The proposed framework outperforms existing methods on two benchmark datasets and achieves state-of-the-art performance. |
SSR: Utilizing Simplified Stance Reasoning Process for Robust Stance Detection (2022.coling-1)
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| Challenge: | Existing methods for stance detection are task-agnostic, which fail to utilize task knowledge to better discriminate between genuine and bias features. |
| Approach: | They propose to incorporate stance reasoning process as task knowledge to aid in learning genuine features without using targets. |
| Outcome: | The proposed model achieves better performance than previous task-agnostic debiasing methods on new test sets. |
Bilateral Masking with prompt for Knowledge Graph Completion (2024.findings-naacl)
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| Challenge: | Existing word matching methods fail to obtain satisfactory single embedding representations for entities. |
| Approach: | They propose a bi-encoder-based approach to enhance entity representations by using prompts to narrow the distance between the predicted entity and the known entity. |
| Outcome: | The proposed model achieves state-of-the-art performance on the WN18RR dataset. |
CoDA: Restoring Contextual Dominance via Copy-Encouraged Attention Intervention for Mitigating RAG Hallucinations (2026.findings-acl)
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| Challenge: | Retrieval-augmented generation reduces hallucination by grounding outputs in external evidence. |
| Approach: | They propose a lightweight inference-time attention intervention that amplifies evidence-aligned value states to enhance contextual faithfulness and reduce hallucination. |
| Outcome: | The proposed model reduces hallucination by grounding model outputs in external evidence. |