Papers by Kaishuai Xu
Reasoning Like a Doctor: Improving Medical Dialogue Systems via Diagnostic Reasoning Process Alignment (2024.findings-acl)
Copied to clipboard
| Challenge: | Medical dialogue systems have attracted significant attention for their potential to act as medical assistants. |
| Approach: | They propose a framework that emulates clinicians' diagnostic reasoning processes and aligns with clinician preferences through thought process modeling. |
| Outcome: | The proposed framework generates appropriate responses that relies on abductive and deductive diagnostic reasoning analyses and aligns with clinician preferences through thought process modeling. |
Subtle Errors in Reasoning: Preference Learning via Error-injected Self-editing (2025.acl-long)
Copied to clipboard
Kaishuai Xu, Tiezheng Yu, Wenjun Hou, Yi Cheng, Chak Tou Leong, Liangyou Li, Xin Jiang, Lifeng Shang, Qun Liu, Wenjie Li
| Challenge: | Existing studies to improve mathematical ability typically involve applying preference learning to step-wise solution pairs, but they overlook critical subtle errors. |
| Approach: | They propose a preference learning framework that injects predefined subtle errors into pivotal tokens to construct hard pairs for error mitigation. |
| Outcome: | Extensive experiments show that the proposed framework improves on Qwen2-7B-Instruct and MATH with 4.5K training samples. |
ORGAN: Observation-Guided Radiology Report Generation via Tree Reasoning (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to generate radiology reports only rely on high-level plans, but they lack important information. |
| Approach: | They propose an Observation-guided radiology Report Generation framework which generates free-text descriptions for a set of radiographs. |
| Outcome: | The proposed framework outperforms state-of-the-art methods regarding text quality and clinical efficacy. |
RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have focused on producing concise observations while neglecting the precise attributes that determine the severity of diseases. |
| Approach: | They propose a model that generates precise radiology reports via dynamic disease progression reasoning by combining historical and spatiotemporal information. |
| Outcome: | Experiments on two publicly available datasets show the proposed model can generate precise and accurate radiology reports with dynamic disease progression reasoning. |
Medical Dialogue Generation via Dual Flow Modeling (2023.findings-acl)
Copied to clipboard
| Challenge: | Medical dialogue systems (MDS) aim to provide patients with medical services, such as diagnosis and prescription. |
| Approach: | They propose a Dual Flow enhanced Medical (DFMed) dialogue generation framework that extracts the medical entities and doctor's dialogue acts used in the dialogue history and models their transitions with an entity-centric graph flow and a sequential act flow. |
| Outcome: | The proposed framework exceeds baselines in both automatic and manual evaluations on two datasets. |
When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detection (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are used for depression detection but their application remains unexplored. |
| Approach: | They propose to integrate acoustic speech information into LLMs for depression detection by integrating aural landmarks into the framework. |
| Outcome: | The proposed method adds critical dimensions to speech transcripts and provides insights into the unique speech patterns of individuals. |
ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to radiology report generation lack inter-report consistency, exhibiting biases towards common patterns and susceptibility to lesion variants. |
| Approach: | They propose a method which improves the inter-report consistency of radiology report generation by extracting lesions from input images and examining their characteristics. |
| Outcome: | The proposed system captures similarities in semantically equivalent lesions and can be used to generate reports for two semantically identical cases. |
RAR2: Retrieval-Augmented Medical Reasoning via Thought-Driven Retrieval (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods focus on refining queries without modeling the reasoning process, limiting their ability to retrieve and integrate clinically relevant knowledge. |
| Approach: | They propose a joint learning framework that improves Reasoning-Augmented Retrieval and Retri-Agmented Reasoning. |
| Outcome: | The proposed model outperforms RAG baselines on biomedical question answering datasets. |
Learning to Align Multi-Faceted Evaluation: A Unified and Robust Framework (2025.findings-acl)
Copied to clipboard
Kaishuai Xu, Tiezheng Yu, Yi Cheng, Wenjun Hou, Liangyou Li, Xin Jiang, Lifeng Shang, Qun Liu, Wenjie Li
| Challenge: | Existing methods for fine-tuning open-source LLMs are limited to text-based analysis under predefined general criteria. |
| Approach: | They propose a framework that fine-tunes LLMs to replicate the evaluation explanations and judgments of proprietary models. |
| Outcome: | The proposed evaluation framework outperforms existing fine-tuned evaluation methods in effectiveness and robustness. |
RADAR: Enhancing Radiology Report Generation with Supplementary Knowledge Injection (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to enhance radiology report generation overlook the knowledge already embedded within the models, leading to redundant information integration. |
| Approach: | They propose a framework for enhancing radiology report generation with supplementary knowledge injection that leverages both internal and external knowledge. |
| Outcome: | Extensive experiments on MIMIC-CXR, CheXpert-Plus, and IU X-ray show that the proposed model outperforms state-of-the-art LLMs in both language quality and clinical accuracy. |