Papers by Yingming Li
When Long Helps Short: How Context Length in Supervised Fine-tuning Affects Behavior of Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have achieved impressive performance across NLP tasks. |
| Approach: | They propose to use long-context SFT to improve short-contemporary performance . they also decouple and analyze two key components, Multi-Head Attention and Feed-Forward Network . |
| Outcome: | The proposed model improves short-context performance, contrary to pretraining. |
Beyond Surface Simplicity: Revealing Hidden Reasoning Attributes for Precise Commonsense Diagnosis (2025.acl-long)
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| Challenge: | Existing commonsense question answering benchmarks often treat these aspects in isolation, resulting in evaluation accuracy differences of up to 24.8% across different difficulty levels. |
| Approach: | They propose a framework that reveals hidden reasoning attributes behind commonsense questions by leveraging the knowledge generated during the reasoning process. |
| Outcome: | The proposed framework reveals hidden reasoning attributes behind commonsense questions by leveraging the knowledge generated during the reasoning process. |
BERT-enhanced Relational Sentence Ordering Network (2020.emnlp-main)
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| Challenge: | Existing approaches to improve coherence modeling for paragraphs have been developed. |
| Approach: | They propose a BERT-enhanced Relational Sentence Ordering Network to capture better dependency relationship among sentences and exploit it with a deep relational module. |
| Outcome: | The proposed model shows significant improvement over the state-of-the-art on six datasets. |
Low-Rank HOCA: Efficient High-Order Cross-Modal Attention for Video Captioning (D19-1)
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| Challenge: | Existing studies on video captioning focus on the association relationships between multiple modalities. |
| Approach: | They propose a video captioning model with high-order cross-modal attention (HOCA) they propose low-rank HOCA which adopts tensor decomposition to reduce the space requirement . |
| Outcome: | The proposed model captures cross-modal interaction of different modalities and reduces space requirement. |
LNE-Blocking: An Efficient Framework for Contamination Mitigation Evaluation on Large Language Models (2025.findings-emnlp)
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| Challenge: | a problem of data contamination is now almost inevitable during the development of large language models, with the training data often integrating evaluation benchmarks even unintentionally. |
| Approach: | They propose a framework to restore model performance prior to data contamination on potentially leaked datasets by using contamination detection and disruption operation. |
| Outcome: | The proposed framework restores model performance prior to contamination on potentially leaked datasets. |
Deep Attentive Sentence Ordering Network (D18-1)
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| Challenge: | Existing methods for sentence ordering tasks rely on linguistic knowledge and are domain specific. |
| Approach: | They propose a deep attentive sentence ordering network which integrates self-attention mechanism with LSTMs in the encoding of input sentences. |
| Outcome: | The proposed model outperforms the state-of-the-art models on Sentence Ordering and Order Discrimination tasks and is shown to be highly efficient. |
Dual Low-Rank Multimodal Fusion (2020.findings-emnlp)
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| Challenge: | Existing tensor-based fusion methods make poor use of fine-grained temporal dynamics of multimodal sequential features. |
| Approach: | They propose a novel multimodal fusion method called Fine-Grained Temporal Low-Rank Multimodal Fusion that uses low-rank tensor approximation along dual dimensions of input features. |
| Outcome: | The proposed method outperforms the state-of-the-art tensor-based methods with a similar computational complexity. |
Fine-tune BERT with Sparse Self-Attention Mechanism (D19-1)
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| Challenge: | Existing sparse self-attention fine-tuning models have been used to improve sentiment analysis, question answering, and natural language inference tasks. |
| Approach: | They propose a Sparse Self-Attention Fine-tuning model which integrates sparsity into self-attention mechanism to enhance the fine-tune performance of BERT. |
| Outcome: | The proposed model outperforms the baseline models on sentiment analysis, question answering, and natural language inference tasks and is able to interpret the input better. |