Papers by Guimin Hu
Debiasing Multilingual LLMs in Cross-lingual Latent Space (2025.emnlp-main)
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| Challenge: | Existing studies have evaluated their cross-lingual transferability by directly applying these methods to LLM representations, revealing their limited effectiveness across languages. |
| Approach: | They propose to perform debiasing in a joint latent space rather than directly on LLM representations by using an autoencoder trained on parallel TED talk scripts. |
| Outcome: | The proposed method improves both the overall debiasing performance and cross-lingual transferability of the proposed techniques across four languages. |
SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated strong performance across various NLP tasks, but their effectiveness in ECI remains limited due to biases in causal reasoning. |
| Approach: | They propose a structural example retrieval framework that leverages LLMs’ few-shot learning capabilities to help LLM models in ECI. |
| Outcome: | The proposed framework leverages LLMs’ few-shot learning capabilities to guide LLM models in causal reasoning, mitigating bias and improving accuracy. |
HapticCap: A Multimodal Dataset and Task for Understanding User Experience of Vibration Haptic Signals (2025.findings-emnlp)
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| Challenge: | a dataset of vibration haptic signals is developed to match descriptions to vibrations . a lack of large datasets annotated with textual descriptions is a challenge . |
| Approach: | They propose a multimodal dataset and task to match user descriptions to vibration haptic signals. |
| Outcome: | The proposed dataset matches user descriptions to vibration haptic signals . the results show that language models and audio models perform better than existing models . |
Bidirectional Hierarchical Attention Networks based on Document-level Context for Emotion Cause Extraction (2021.findings-emnlp)
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| Challenge: | Emotion cause extraction (ECE) aims to extract the causes behind certain emotion in text. |
| Approach: | They propose a bidirectional hierarchical attention network corresponding to the specified candidate cause clause to capture document-level context in a structured and dynamic manner. |
| Outcome: | The proposed method achieves competitive performances on two public datasets in Chinese and English. |
Towards Multi-modal Sarcasm Detection via Disentangled Multi-grained Multi-modal Distilling (2024.lrec-main)
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| Challenge: | Existing approaches to sarcasm detection focus on textual and intra-modal incongruity . mainstream approaches process input of each modality in a holistic manner, resulting in redundant and unrefined information. |
| Approach: | They propose a framework for multi-modal sarcasm detection that disentangles modality representations into latent spaces and conducts multi-grained knowledge distilling. |
| Outcome: | The proposed framework overpowers existing methods on a common benchmark. |
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition (2022.emnlp-main)
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| Challenge: | Existing studies study sentiment and emotion separately and do not fully exploit the complementary knowledge behind the two. |
| Approach: | They propose a multimodal sentiment knowledge-sharing framework that unifies MSA and ERC tasks from features, labels, and models. |
| Outcome: | The proposed framework achieves consistent improvements on four public benchmark datasets on MOSI, MOSEI, MELD, and IEMOCAP. |
FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture (2024.emnlp-main)
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Wenyan Li, Crystina Zhang, Jiaang Li, Qiwei Peng, Raphael Tang, Li Zhou, Weijia Zhang, Guimin Hu, Yifei Yuan, Anders Søgaard, Daniel Hershcovich, Desmond Elliott
| Challenge: | FoodieQA is a manually curated, fine-grained image-text dataset capturing the intricate features of food cultures across various regions in China. |
| Approach: | They evaluate vision–language Models and large language models on unseen food images and corresponding questions. |
| Outcome: | The proposed dataset evaluates vision–language Models and large language models on unseen food images and corresponding questions. |
UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause (2024.findings-emnlp)
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| Challenge: | Existing studies treat emotion recognition and emotion cause extraction as two individual problems, ignoring their natural causality. |
| Approach: | They propose a Unified Multimodal Emotion recognition and Emotion-Cause analysis framework to explore the causality between emotion and emotion cause. |
| Outcome: | The proposed framework reformulates MERC and MECPE tasks as mask prediction problems and unifies them with a causal prompt template. |
HapticLLaMA: A Multimodal Sensory Language Model for Haptic Captioning (2026.findings-eacl)
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| Challenge: | haptic captioning is the task of generating natural language descriptions from haptics, such as vibrations, for use in virtual reality and rehabilitation applications. |
| Approach: | They propose a multimodal sensory language model that interprets vibration signals into descriptions in a given sensory, emotional, or associative category. |
| Outcome: | The proposed model interprets vibration signals into descriptions in a given sensory, emotional, or associative category. |