Papers by Songtao Liu
HSCR: Hierarchical Self-Contrastive Rewarding for Aligning Medical Vision Language Models (2025.acl-long)
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| Challenge: | Existing methods for medical vision-language models overlook modality misalignment . HSCR generates high-quality preference data with higher sampling probability . |
| Approach: | They propose a hierarchical self-contrastive reward approach that addresses two challenges in alignment . they leverage the inherent capability of Med-VLMs to generate dispreferred responses . |
| Outcome: | The proposed approach improves accuracy and trustworthiness of medical vision-label models with 2,000 training entries. |
Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models (2024.findings-emnlp)
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| Challenge: | Recent advances in multimodal large language models have seen remarkable progress for medical decision-making, however, they are designated for specific classification or generative tasks and require model training or finetuning on large-scale datasets with sizeable parameters and tremendous computing. |
| Approach: | They propose a framework that tackles discriminative and generative multimodal medical tasks using multimodal alignment, instruction tuning and routing. |
| Outcome: | The proposed model can achieve superior performance to or on par with state-of-the-art baselines while only requiring 30%-50% of activated model parameters. |
Act as you think: Reinforcing Consistent Reasoning in Medical Visual Question Answering (2026.acl-long)
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Songtao Jiang, Yuan Wang, Ruizhe Chen, Yan Zhang, Ruilin Luo, Bohan Lei, Yeying Jin, Sibo Song, ZhiBo Yang, Jimeng Sun, Jian Wu, Zuozhu Liu
| Challenge: | Recent advances have improved the accuracy of medical visual question answering (Med-VQA) however, the high stakes nature of the medical domain has precipitated a shift towards interpretability and transparency of reasoning processes. |
| Approach: | They propose a reinforcement learning from verifiable rewards framework that rewards internal consistency and logical coherence. |
| Outcome: | The proposed framework rewards internal consistency and logical coherence, and is highly versatile, the authors show. |
Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering (2025.acl-short)
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| Challenge: | Current approaches generate visual markers for all questions, generating excessive visual markers. |
| Approach: | They propose a plug-and-play approach that adapts to the complexity of questions . they propose combining fast intuitive judgments with deliberate analytical reasoning . |
| Outcome: | The proposed approach improves performance on four benchmarks on ScienceQA, TextQA, VizWiz, and MME. |
Encoding Hierarchical Schema via Concept Flow for Multifaceted Ideology Detection (2024.findings-acl)
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| Challenge: | Existing studies on ideology detection focus on one generic facet and ignore label semantics and explanatory descriptions of ideologies. |
| Approach: | They propose a concept semantics-enhanced framework for multifaceted ideology detection . it enables concepts to flow across levels of the schema tree and enriches concept representations with multi-granularity semantics. |
| Outcome: | The proposed framework achieves state-of-the-art in the cross-topic scenario and on the benchmark dataset. |
Benchmarking Large Language Models on Communicative Medical Coaching: A Dataset and a Novel System (2024.findings-acl)
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| Challenge: | Existing applications of natural language processing (NLP) focus on patient-centered services, but the potential of NLP to benefit inexperienced doctors remains unexplored. |
| Approach: | They propose a human-AI cooperative framework to assist medical learners in practicing communication skills during patient consultations. |
| Outcome: | The proposed framework enables medical learners to practice communication skills during patient consultations while a coach agent provides immediate, structured feedback. |
Ideology Takes Multiple Looks: A High-Quality Dataset for Multifaceted Ideology Detection (2023.emnlp-main)
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| Challenge: | Existing datasets for the ID task only label a text as ideologically left- or right-leaning as a whole, regardless whether the text containing one or more different issues. |
| Approach: | They construct an ideological schema for a multifaceted ideology detection task using MITweet and an English Twitter dataset. |
| Outcome: | The proposed task uses a MITweet dataset with 12,594 English Twitter posts, each annotated with a Relevance and an Ideology label for all twelve facets. |