Papers by Ziqi Jia
Sailor: Open Language Models for South-East Asia (2024.emnlp-demo)
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| Challenge: | Large language models (LLMs) rely on English data for training, but are often not comparable across other languages. |
| Approach: | They propose to develop a family of open language models for SEA languages . they use BPE dropout, aggressive data cleaning and deduplication to improve model robustness . |
| Outcome: | The proposed models perform well across four benchmarks, including commonsense reasoning, question answering, reading comprehension and examination. |
MAViS: A Multi-Agent Framework for Long-Sequence Video Storytelling (2026.eacl-long)
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| Challenge: | Experimental results show that MAViS achieves state-of-the-art performance in assistive capability, visual quality, and video expressiveness. |
| Approach: | They propose an end-to-end multi-agent collaborative framework for long-sequence video storytelling that orchestrates specialized agents across multiple stages. |
| Outcome: | The proposed framework achieves state-of-the-art performance in assistive capability, visual quality, and video expressiveness. |
Revisiting LoRA through the Lens of Parameter Redundancy: Spectral Encoding Helps (2025.findings-acl)
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| Challenge: | Low-Rank Adaptation (LoRA) has emerged as a prominent technique for fine-tuning large foundation models. |
| Approach: | They propose a low-rank Adaptation technique that harnesses the expressiveness of spectral bases to re-parameterize LoRA from a sparse spectral subspace. |
| Outcome: | The proposed technique achieves greater efficiency with fewer parameters than baselines on various downstream tasks, including commonsense reasoning, math reasoning, and code generation. |
Hierarchical-Task-Aware Multi-modal Mixture of Incremental LoRA Experts for Embodied Continual Learning (2025.acl-long)
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| Challenge: | Existing continual learning setups for embodied intelligence focus on executing low-level actions, neglecting the ability to learn high-level planning and multi-level knowledge. |
| Approach: | They propose a Hierarchical Embodied Continual Learning Setups (HEC) that divides the agent’s continual learning process into two layers: high-level instructions and low-level actions. |
| Outcome: | The proposed method reduces the forgetting of old tasks compared to other methods, while orthogonally training the remaining parts. |
ReContraster: Making Your Posters Stand Out with Regional Contrast (2026.acl-long)
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| Challenge: | Effective poster design requires rapidly capturing attention and clearly conveying messages. |
| Approach: | They propose a poster-based model that leverages regional contrast to make posters stand out. |
| Outcome: | The proposed model outperforms state-of-the-art methods in producing striking posters. |
Parameter-Efficient Fine-Tuning via Circular Convolution (2025.findings-acl)
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| Challenge: | Low-Rank Adaptation (LoRA) has gained popularity for fine-tuning large foundation models, but its intrinsic low-rank characteristic may limit its performance. |
| Approach: | They propose a low-rank adaptive method that uses low-ranked matrices to represent weight changes. |
| Outcome: | The proposed method reduces trainable parameters and mitigates heavy memory consumption associated with full delta matrices by sequentially multiplying mathbf A and mathbb B with the activation. |