Search Results for author: Mozhi Zhang

Found 19 papers, 11 papers with code

Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training

no code implementations12 Jun 2025 Mozhi Zhang, Howe Tissue, Lu Wang, Xipeng Qiu

\textsc{Domain2Vec} maintains a vocabulary of meta-domains and uses a classifier to decompose any given dataset into a domain vector that corresponds to a distribution over this vocabulary.

LongSafetyBench: Long-Context LLMs Struggle with Safety Issues

1 code implementation11 Nov 2024 Mianqiu Huang, Xiaoran Liu, Shaojun Zhou, Mozhi Zhang, Chenkun Tan, Pengyu Wang, Qipeng Guo, Zhe Xu, Linyang Li, Zhikai Lei, Linlin Li, Qun Liu, Yaqian Zhou, Xipeng Qiu, Xuanjing Huang

With the development of large language models (LLMs), the sequence length of these models continues to increase, drawing significant attention to long-context language models.

MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time

1 code implementation18 Oct 2024 Mozhi Zhang, Pengyu Wang, Chenkun Tan, Mianqiu Huang, Dong Zhang, Yaqian Zhou, Xipeng Qiu

Large Language Models (LLMs) acquire extensive knowledge and remarkable abilities from extensive text corpora, making them powerful tools for various applications.

Diversity

Labeled Interactive Topic Models

no code implementations15 Nov 2023 Kyle Seelman, Mozhi Zhang, Jordan Boyd-Graber

To facilitate user interaction with these neural topic models, we have developed an interactive interface.

Topic Models

PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search

1 code implementation20 May 2023 Mozhi Zhang, Hang Yan, Yaqian Zhou, Xipeng Qiu

We use prompts that contains entity category information to construct label prototypes, which enables our model to fine-tune with only the support set.

few-shot-ner Few-shot NER +4

Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth

no code implementations10 May 2021 Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, Kenji Kawaguchi

Our results show that the training of GNNs is implicitly accelerated by skip connections, more depth, and/or a good label distribution.

How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels?

no code implementations NeurIPS 2021 Jingling Li, Mozhi Zhang, Keyulu Xu, John P. Dickerson, Jimmy Ba

Our framework measures a network's robustness via the predictive power in its representations -- the test performance of a linear model trained on the learned representations using a small set of clean labels.

Learning with noisy labels

How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

3 code implementations ICLR 2021 Keyulu Xu, Mozhi Zhang, Jingling Li, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka

Second, in connection to analyzing the successes and limitations of GNNs, these results suggest a hypothesis for which we provide theoretical and empirical evidence: the success of GNNs in extrapolating algorithmic tasks to new data (e. g., larger graphs or edge weights) relies on encoding task-specific non-linearities in the architecture or features.

Interactive Refinement of Cross-Lingual Word Embeddings

1 code implementation EMNLP 2020 Michelle Yuan, Mozhi Zhang, Benjamin Van Durme, Leah Findlater, Jordan Boyd-Graber

Cross-lingual word embeddings transfer knowledge between languages: models trained on high-resource languages can predict in low-resource languages.

Active Learning Cross-Lingual Word Embeddings +3

Are Girls Neko or Sh\=ojo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization

no code implementations ACL 2019 Mozhi Zhang, Keyulu Xu, Ken-ichi Kawarabayashi, Stefanie Jegelka, Jordan Boyd-Graber

Cross-lingual word embeddings (CLWE) underlie many multilingual natural language processing systems, often through orthogonal transformations of pre-trained monolingual embeddings.

Cross-Lingual Word Embeddings Translation +2

Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization

1 code implementation4 Jun 2019 Mozhi Zhang, Keyulu Xu, Ken-ichi Kawarabayashi, Stefanie Jegelka, Jordan Boyd-Graber

Cross-lingual word embeddings (CLWE) underlie many multilingual natural language processing systems, often through orthogonal transformations of pre-trained monolingual embeddings.

Cross-Lingual Word Embeddings Translation +2

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