Search Results for author:
Found 19 papers, 11 papers with code
Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training
\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.
SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond
In our experiments, we validate the effectiveness of RL training on the SynLogic dataset based on 7B and 32B models.
MiniMax-01: Scaling Foundation Models with Lightning Attention
This approach enables us to conduct efficient training and inference on models with hundreds of billions of parameters across contexts spanning millions of tokens.
LongSafetyBench: Long-Context LLMs Struggle with Safety Issues
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
Large Language Models (LLMs) acquire extensive knowledge and remarkable abilities from extensive text corpora, making them powerful tools for various applications.
Calibrating the Confidence of Large Language Models by Eliciting Fidelity
Large language models optimized with techniques like RLHF have achieved good alignment in being helpful and harmless.
Labeled Interactive Topic Models
To facilitate user interaction with these neural topic models, we have developed an interactive interface.
Evaluating Hallucinations in Chinese Large Language Models
We analyze the primary types of hallucinations in different types of models and their causes.
PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search
We use prompts that contains entity category information to construct label prototypes, which enables our model to fine-tune with only the support set.
A Dataset and Baselines for Multilingual Reply Suggestion
Reply suggestion models help users process emails and chats faster.
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth
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?
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.
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
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.
Why Overfitting Isn't Always Bad: Retrofitting Cross-Lingual Word Embeddings to Dictionaries
Cross-lingual word embeddings (CLWE) are often evaluated on bilingual lexicon induction (BLI).
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+2
Interactive Refinement of Cross-Lingual Word Embeddings
Cross-lingual word embeddings transfer knowledge between languages: models trained on high-resource languages can predict in low-resource languages.
Are Girls Neko or Sh\=ojo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization
Cross-lingual word embeddings (CLWE) underlie many multilingual natural language processing systems, often through orthogonal transformations of pre-trained monolingual embeddings.
Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization
Cross-lingual word embeddings (CLWE) underlie many multilingual natural language processing systems, often through orthogonal transformations of pre-trained monolingual embeddings.
Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification
Text classification must sometimes be applied in a low-resource language with no labeled training data.
Cross-Lingual Document Classification
Document Classification
+3
