Papers by Tingyu Qu
Mitigating Negative Interference in Multilingual Knowledge Editing through Null-Space Constraints (2025.findings-acl)
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| Challenge: | Existing monolingual knowledge editing methods are expensive and require multiple models to maintain factual consistency. |
| Approach: | They propose a null-space constrained framework to precisely isolate language-specific knowledge updates that can be mapped onto other languages’ subspaces. |
| Outcome: | The proposed framework can project parameter updates for each language onto the orthogonal complement of other languages’ subspaces while preserving multilingual generalization capabilities. |
Visually-Aware Context Modeling for News Image Captioning (2024.naacl-long)
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| Challenge: | a new framework for News Image Captioning emphasizes the connection between textual context and visual elements. |
| Approach: | They propose a face-naming module for learning better name embeddings from news images . they use CLIP to retrieve sentences that are semantically close to the image . |
| Outcome: | The proposed framework outperforms the current state-of-the-art by 7.97/5.80 CIDEr scores on GoodNews/NYTimes800k. |
OASIS: Online Sample Selection for Continual Instruction Tuning (2026.acl-long)
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| Challenge: | Existing methods for continual instruction tuning (CIT) use pre-trained reference models, which are impractical in CIT setups since future data are unknown. |
| Approach: | They propose an adaptive online sample selection approach that estimates each sample’s informativeness relative to all previously seen data and minimizes informative redundancy through iterative selection score updates. |
| Outcome: | Experiments on various large foundation models show that using only 25% of the data achieves comparable performance to full-data training and outperforms the state-of-the-art sampling methods. |