Papers by Jiaxiang Wu
Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models (2025.acl-demo)
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Jiaxiang Liu, Boxuan Xing, Chenhao Yuan, ChenxiangZhang ChenxiangZhang, Di Wu, Xiusheng Huang, Haida Yu, Chuhan Lang, Pengfei Cao, Jun Zhao, Kang Liu
| Challenge: | Existing interpretation methods only support tasks with specific inputs, limiting their practical applications. |
| Approach: | They propose an extensible module that matches different input data with interpretation methods and consolidates the interpreting outputs. |
| Outcome: | The proposed module can match different input data with interpretation methods and consolidate the interpreting outputs. |
VPL: Visual Proxy Learning Framework for Zero-Shot Medical Image Diagnosis (2024.findings-emnlp)
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| Challenge: | Insufficient medical text precision and the modal disparity between text and vision spaces pose challenges for vision-language models like CLIP. |
| Approach: | They propose a visual proxy learning framework that combines a text refinement module and a stable Sinkhorn algorithm to enhance the diagnostic performance. |
| Outcome: | The proposed model outperforms the state-of-the-art CLIP inference by 1.69% to 15.31% on five datasets covering various diseases. |
BMAM: Brain-inspired Multi-Agent Memory Framework (2026.findings-acl)
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| Challenge: | Language-model-based agents operating over extended interactions face persistent challenges in preserving temporally grounded information and maintaining behavioral consistency across sessions. |
| Approach: | They propose a general-purpose memory architecture that decomposes agent memory into six components that operate at complementary time scales. |
| Outcome: | BMAM outperforms memory-augmented baselines on LoCoMo benchmarks with 78.45% accuracy . a targeted refinement of the temporal-trigger heuristics raises LongMemEval multi-session accuracy from 45.2% to 56.4%. |
PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection (2023.findings-emnlp)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated remarkable zero-shot performance across various NLP tasks. |
| Approach: | They propose a method which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner and prompts an LLM to rate individual items at each turn. |
| Outcome: | The proposed method improves the performance and robustness of the standard GPT-3.5 personality detection task on two benchmark datasets. |
MedThink: A Rationale-Guided Framework for Explaining Medical Visual Question Answering (2025.findings-naacl)
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| Challenge: | Existing models for medical visual question answering are limited in their interpretation and interpretation . a semi-automated annotation process is used to streamline data preparation and build new benchmark datasets . |
| Approach: | They propose a semi-automated annotation process to streamline data preparation and build new benchmark Med-VQA datasets. |
| Outcome: | The proposed method achieves an accuracy of 83.5% on R-RAD, 86.3% on RSLAKE and 87.2% on RPath. |