Sentence Embedding
132 papers with code • 0 benchmarks • 7 datasets
Benchmarks
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Libraries
Use these libraries to find Sentence Embedding models and implementations
Tatoeba
UR-FUNNY
Video2GIF
Latest papers with no code
Missed Connections: Lateral Thinking Puzzles for Large Language Models
This is because the four categories ascend in complexity, with the most challenging category often requiring thinking about words in uncommon ways or as parts of larger phrases.
Is Next Token Prediction Sufficient for GPT? Exploration on Code Logic Comprehension
Our experimental results reveal that following this pretraining, both Code Llama and StarCoder, the prevalent code domain pretraining models, display significant improvements on our logically equivalent code selection task and the code completion task.
Enhancing Cross-lingual Sentence Embedding for Low-resource Languages with Word Alignment
The field of cross-lingual sentence embeddings has recently experienced significant advancements, but research concerning low-resource languages has lagged due to the scarcity of parallel corpora.
Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual Applications
Prior work on multilingual sentence embedding has demonstrated that the efficient use of natural language inference (NLI) data to build high-performance models can outperform conventional methods.
Adaptative Bilingual Aligning Using Multilingual Sentence Embedding
In this paper, we present an adaptive bitextual alignment system called AIlign.
RobustSentEmbed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive Learning
In this paper, we introduce RobustSentEmbed, a self-supervised sentence embedding framework designed to improve both generalization and robustness in diverse text representation tasks and against a diverse set of adversarial attacks.
Improving Sentence Embeddings with an Automatically Generated NLI Dataset
Decoder-based large language models (LLMs) have shown high performance on many tasks in natural language processing.
2D Matryoshka Sentence Embeddings
The experimental results demonstrate the effectiveness of our proposed model in dynamically supporting different embedding sizes and Transformer layers, allowing it to be highly adaptable to various scenarios.
TexShape: Information Theoretic Sentence Embedding for Language Models
With the exponential growth in data volume and the emergence of data-intensive applications, particularly in the field of machine learning, concerns related to resource utilization, privacy, and fairness have become paramount.
LSTM-based Deep Neural Network With A Focus on Sentence Representation for Sequential Sentence Classification in Medical Scientific Abstracts
For this reason, the role of sentence embedding is crucial for capturing both the semantic information between words in the sentence and the contextual relationship of sentences within the abstract to provide a comprehensive representation for better classification.
