Papers by Shuzhen Li
Incongruity-aware Tension Field Network for Multi-modal Sarcasm Detection (2025.acl-long)
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| Challenge: | Multi-modal sarcasm detection (MSD) identifies sarcasm and accurately understands users’ real attitudes from text-image pairs. |
| Approach: | They propose to use incongruity-aware tension field network to extract effective text-image feature pairs in fact and sentiment perspectives and construct a fact/sentiment tension field with discrepancy metrics to capture contextual tone and polarized inconcongruities. |
| Outcome: | The proposed method achieves state-of-the-art performance surpassing LLaVA1.5-7B with only 17.3M trainable parameters, demonstrating its optimal performance-efficiency in multi-modal sarcasm detection tasks. |
A Parameter-Efficient and Fine-Grained Prompt Learning for Vision-Language Models (2025.acl-long)
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| Challenge: | Current vision-language models extract semantic information from large-scale cross-modal associations, limiting performance and efficiency. |
| Approach: | They propose a detail-oriented prompt learning method to implement fine-grained multi-modal semantic alignment with merely 0.25M trainable parameters. |
| Outcome: | The proposed method implements fine-grained multi-modal semantic alignment with merely 0.25M trainable parameters. |
Multi-Scale Prompt Memory-Augmented Model for Black-Box Scenarios (2024.naacl-long)
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| Challenge: | Existing methods for few-shot text classification require numerous LMs’ calls to search optimal prompts, thus resulting in overfitting performance and increasing computational cost. |
| Approach: | They propose a multi-scale knowledge prompt-based memory model that extracts instance-level and class-level knowledge and stores them in memory banks during training. |
| Outcome: | Experiments on different benchmarks and parameter analysis demonstrate the effectiveness and efficiency of MuSKPrompt in black-box few-shot text classification tasks. |
An Orthogonal High-Rank Adaptation for Large Language Models (2025.emnlp-main)
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| Challenge: | Low-rank adaptation (LoRA) efficiently adapts LLMs to downstream tasks by decomposing LLM’s weight update into trainable low-rank matrices for fine-tuning. |
| Approach: | They propose an orthogonal high-rank adaptation for parameter-efficient fine-tuning that decomposes LLMs’ pre-trained weight matrices into orthogonals via QR decomposition and splits them into two low-redundancy high-ranked components. |
| Outcome: | Empirical results show that OHoRA outperforms LoRA and its variants and generates task-tailored representation spaces with 0.0371% trainable parameters. |