Papers by Kangqi Ni
Learning Flexible Large Multimodal Models with Arbitrary Modality Combinations (2026.findings-acl)
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| Challenge: | Multimodal Large Language Models (MLLMs) have potential for cross-modal understanding . but extending MLLM to handle diverse modalities introduces two challenges . |
| Approach: | They propose a dual-stage compression mechanism to reduce the number of modality tokens per modality and condense it into a single, compact token sequence. |
| Outcome: | Experiments show that Flex-M3 outperforms its counterpart trained on only full-modality data. |
Pedagogical Alignment of Large Language Models (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are often used without pedagogical fine-tuning and provide immediate answers rather than guiding students through the problem-solving process. |
| Approach: | They propose a method for constructing large-scale preference datasets using synthetic data generation techniques that eliminates the need for manual annotation. |
| Outcome: | The proposed methods outperform standard supervised fine-tuning (SFT) and improve alignment accuracy by 13.1% and 8.7% respectively. |
Anchor: Branch-Point Data Generation for GUI Agents (2026.acl-long)
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| Challenge: | Existing GUI agents for real desktop environments require large amounts of high-quality interaction data, but collecting human demonstrations is expensive. |
| Approach: | They propose a framework that bootstraps scalable desktop supervision from seed demonstrations. |
| Outcome: | Experiments on standard desktop benchmarks show that the framework improves on zero-shot agents and representative synthesis baselines. |