Papers by Chengfeng Zhao
Detection, Diagnosis, and Explanation: A Benchmark for Chinese Medial Hallucination Evaluation (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have made significant progress in recent years, but their practical use is hindered by their tendency to generate hallucinations. |
| Approach: | They propose to use ICD-10 and MeSH to evaluate LLMs' ability to detect medical hallucinations and make accurate diagnoses in noisy environments. |
| Outcome: | The proposed benchmark can be used to evaluate LLMs’ ability to detect medical hallucinations, make accurate diagnoses in noisy conditions, and provide plausible explanations. |
Why and How LLMs Benefit from Knowledge Introspection in Commonsense Reasoning (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) can improve commonsense reasoning by generating intermediate knowledge, but the effectiveness of this knowledge introspection is not always guaranteed. |
| Approach: | They propose a training-free strategy that optimizes introspection via two stages: Knowledge Detection and Knowledge Regeneration. |
| Outcome: | The proposed approach mitigates the limitations of standard introspection and has consistent performance gains across all settings. |
Integrating Physician Diagnostic Logic into Large Language Models: Preference Learning from Process Feedback (2024.findings-acl)
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| Challenge: | Existing studies have shown that large language models can enhance response richness and coherence, but there is a pressing need to bolster the model’s capacity for diagnostic logic to ensure patient safety. |
| Approach: | They propose an approach termed preference learning from process feedback (PLPF) that integrates the doctor’s diagnostic logic into LLMs. |
| Outcome: | The proposed approach improves the diagnostic accuracy of the baseline model in medical conversations by 17.6%, surpassing the performance of traditional approaches. |
SEAG: Structure-Aware Event Causality Generation (2023.findings-acl)
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Zhengwei Tao, Zhi Jin, Xiaoying Bai, Haiyan Zhao, Chengfeng Dou, Yongqiang Zhao, Fang Wang, Chongyang Tao
| Challenge: | Current methods for extracting event causality are limited by the lack of cross-task dependencies and may cause error propagation. |
| Approach: | They propose an approach for Structure-Aware Event Causality Generation (SEAG) they generate the ECG structure using a pre-trained language model and perform structural discriminative training alongside auto-regressive generation. |
| Outcome: | The proposed method is effective in extracting event causality from text. |
UniEvent: Unified Generative Model with Multi-Dimensional Prefix for Zero-Shot Event-Relational Reasoning (2023.acl-long)
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| Challenge: | Reasoning about events and their relations is an indispensable ability to fulfill various event-centric or common-sense reasoning tasks. |
| Approach: | They propose a multi-task learning framework that organizes event relational reasoning tasks into a coordinate system with multiple axes, representing inter-event relations and reasoning formulations. |
| Outcome: | The proposed framework achieves state-of-the-art or competitive performance on zero-shot and supervised reasoning tasks. |
PlugMed: Improving Specificity in Patient-Centered Medical Dialogue Generation using In-Context Learning (2023.findings-emnlp)
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| Challenge: | In-context learning is a key task in health conversational assistants, but it is difficult to guarantee the specificity of the responses. |
| Approach: | They propose a plug-and-play medical dialogue system that provides a patient-centered medical interpretation service to users who are less knowledgeable about medical knowledge. |
| Outcome: | The proposed model improves the specificity of the patient-centered medical dialogues by providing them with real dialogues from similar patients as prompts. |