Papers by Wenhan Luo
A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation (2024.lrec-main)
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| Challenge: | Existing methods for generating product descriptions from images are inaccurate and generic . e-commerce product descriptions are important for content marketing and increasing engagement . |
| Approach: | They propose a new setting for generating product descriptions from images, augmented by marketing keywords. |
| Outcome: | The proposed approach improves the accuracy and diversity of product descriptions by up to 3.3% on Rouge-L and 9.4% on D-5. |
AGSC: Adaptive Granularity and Semantic Clustering for Uncertainty Quantification in Long-text Generation (2026.acl-long)
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| Challenge: | Existing methods for aggregating large-form outputs overlook the nuance of neutral information and suffer from the high computational cost of fine-grained decomposition. |
| Approach: | They propose a UQ framework that uses NLI neutral probabilities as triggers to distinguish irrelevance from uncertainty, reducing computation costs. |
| Outcome: | Experiments on BIO and LongFact show that the proposed framework reduces inference time by 60% compared to full atomic decomposition. |
Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video (P19-1)
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| Challenge: | Existing techniques for weakly-supervised spatio-temporally grounding natural sentence in video are lacking . |
| Approach: | They propose a weakly-supervised task for spatially grounding sentences in video . they extract instances from video and encode them using attentive interactor . results demonstrate superiority of their proposed task over baseline approaches . |
| Outcome: | The proposed model outperforms baseline approaches in a weakly-supervised task . it can characterize reliable instance-sentence pairs and penalize unreliable ones . |
Characterizing and Verifying Scientific Claims: Qualitative Causal Structure is All You Need (2023.emnlp-main)
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| Challenge: | a scientific claim verification requires thorough examination and assessment to ascertain its validity . attention architectures and pre-trained language models fail to establish a comprehensive chain of causal inference . |
| Approach: | They propose a qualitative causal structure-based graph neural network model to facilitate causal reasoning across relevant causally-potent factors. |
| Outcome: | The proposed model outperforms state-of-the-art models by incorporating semantic features . the proposed model is based on a qualitative causal structure . |
Identifying Principals and Accessories in a Complex Case based on the Comprehension of Fact Description (2020.acl-main)
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| Challenge: | Existing studies on complex criminal cases with multiple defendants only focus on the simple cases with one defendant. |
| Approach: | They propose to model the defendants with behavioral semantic information and statistical characteristics, then learning the importances of defendants within a learning-to-rank framework. |
| Outcome: | The proposed model can model the defendants’ impacts in a complex case. |
Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges (2024.findings-acl)
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Bosheng Ding, Chengwei Qin, Ruochen Zhao, Tianze Luo, Xinze Li, Guizhen Chen, Wenhan Xia, Junjie Hu, Anh Tuan Luu, Shafiq Joty
| Challenge: | Data augmentation (DA) is a key technique for enhancing model performance by diversifying training examples without the need for additional data collection. |
| Approach: | They examine various strategies that utilize LLMs for data augmentation, including a novel exploration of learning paradigms where LLM-generated data is used for diverse forms of further training. |
| Outcome: | The proposed approach addresses the primary open challenges faced by LLMs in the field of large language models and aims to serve as a comprehensive guide for researchers and practitioners. |
Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions (N18-1)
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| Challenge: | Existing work on court view generation from fact descriptions has improved the working efficiency of legal assistant systems. |
| Approach: | They propose to decode court views conditioned on encoded charge labels from the fact description in a criminal case to improve interpretability of charge prediction systems. |
| Outcome: | The proposed model can generate court views conditioned on encoded charge labels. |
VideoVista-CulturalLingo: 360° Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension (2025.acl-long)
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| Challenge: | Existing video evaluation benchmarks focus on a single language, typically English, and feature videos rooted in Western cultural contexts. |
| Approach: | They propose a video evaluation benchmark designed to bridge cultural, linguistic, and domain divide in video comprehension. |
| Outcome: | The proposed video evaluation benchmark bridges cultural, linguistic, and domain divides . existing benchmarks only feature videos from YouTube, Shutterstock, or established video datasets based on cultural diversity . |