Papers by Zibo Zhang
NeighXLM: Enhancing Cross-Lingual Transfer in Low-Resource Languages via Neighbor-Augmented Contrastive Pretraining (2025.findings-emnlp)
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| Challenge: | NeighXLM is a neighbor-augmented contrastive pretraining framework . it exploits intra-language semantic relationships captured during pretraining to construct high-quality positive pairs. |
| Approach: | They propose a neighbor-augmented contrastive pretraining framework that mines semantic neighbors from unlabeled corpora to enrich target-language supervision. |
| Outcome: | The proposed framework enriches target-language supervision by mining semantic neighbors from unlabeled corpora. |
Principled Understanding of Generalization for Generative Transformer Models in Arithmetic Reasoning Tasks (2025.acl-long)
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| Challenge: | Existing models excel in arithmetic reasoning but their generalization capabilities are incompletely understood. |
| Approach: | They propose a theoretical framework for understanding the generalization behaviors of transformers in arithmetic tasks, focusing on length generalization. |
| Outcome: | The proposed framework can predict generalization behaviors in transformers with a high translation invariance and base mismatch in modular operations. |
SceneGenAgent: Precise Industrial Scene Generation with Coding Agent (2025.acl-long)
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| Challenge: | Recent work on scene generation focuses on generating 3D scenes from textual descriptions . however, the task of generating industrial scenes with LLMs is complex and requires precise measurements and positioning . |
| Approach: | They propose an LLM-based agent for generating industrial scenes through C# code. |
| Outcome: | Experiments show that LLMs powered by SceneGenAgent exceed their original performance . the agent achieves 81.0% success rate in real-world industrial scene generation tasks . |
Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have enabled the development of powerful autonomous systems. |
| Approach: | They propose a model trained through dialectical alignment to enforce perspective-invariant reasoning. |
| Outcome: | The proposed model mitigates attribution inconsistency and significantly improves fault resolution rates in ambiguous scenarios. |