Papers by Kaiyan Zhao
NeoAMT: Neologism-Aware Agentic Machine Translation with Reinforcement Learning (2026.acl-long)
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| Challenge: | Neologism-aware machine translation aims to translate source sentences containing neologismes into target languages. |
| Approach: | They propose an agentic framework for neologism-aware machine translation equipped with a Wiktionary-based search toolkit. |
| Outcome: | The proposed framework is based on a Wiktionary-based search toolkit and a dedicated dataset for neologism-aware machine translation. |
SciSketch: An Open-source Framework for Automated Schematic Diagram Generation in Scientific Papers (2025.emnlp-demos)
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| Challenge: | SCISKETCH is an open-source framework that supports two automated workflows for schematic diagram generation using foundation models. |
| Approach: | They propose an open-source framework that supports two automated workflows for schematic diagram generation using foundation models. |
| Outcome: | The open-source framework outperforms several state-of-the-art foundation models in generating schematic diagrams for scientific papers. |
Leveraging Multi-lingual Positive Instances in Contrastive Learning to Improve Sentence Embedding (2024.eacl-long)
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| Challenge: | Recent trends in learning monolingual and multilingual sentence embeddings are based on contrastive learning (CL) among an anchor, one positive and multiple negative instances. |
| Approach: | They propose to leverage multiple positives to improve learning of multilingual sentence embeddings by using an anchor, one positive, and multiple negative instances. |
| Outcome: | The proposed approach improves retrieval, semantic similarity, and classification performance on unseen languages. |
Fusing Highly Specialized Language Models for Comprehensive Expertise (2025.acl-long)
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Ning Ding, Yulin Chen, Ganqu Cui, Xingtai Lv, Weilin Zhao, Kaiyan Zhang, Ruobing Xie, Bowen Zhou, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing models that focus on language, programming code, and mathematical symbols are not able to achieve mastery of all three domains simultaneously. |
| Approach: | They propose to fuse highly-specialized models that are already sufficiently trained on different domains to achieve a highly-specific model. |
| Outcome: | The proposed model could achieve mastery of the three crucial domains simultaneously. |
Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future (2026.acl-long)
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Sihong Wu, Owen Jiang, Yilun Zhao, Tiansheng Hu, Yiling Ma, Kaiyan Zhang, Manasi Patwardhan, Arman Cohan
| Challenge: | Recent advances in large language models (LLMs) motivated methods that assist or automate different stages of peer review pipeline. |
| Approach: | They synthesize techniques to enhance peer review generation and after-review tasks aligned to reviews. |
| Outcome: | The proposed methods improve the peer review process by fine-tuning strategies, agent-based systems, and emerging paradigms. |
Enhancing Cross-lingual Sentence Embedding for Low-resource Languages with Word Alignment (2024.findings-naacl)
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| Challenge: | Current approaches to obtain cross-lingual sentence embeddings rely on pre-trained language models that implicitly align the contextual representations of similar units of sentences in different languages. |
| Approach: | They propose a framework that explicitly aligns words between English and eight low-resource languages by using off-the-shelf word alignment models. |
| Outcome: | The proposed framework improves on the bitext retrieval task and in high-resource languages. |