Sentence Embedding
132 papers with code • 0 benchmarks • 7 datasets
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Libraries
Use these libraries to find Sentence Embedding models and implementationsLatest papers
Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistance
Our results demonstrate that there is no significant difference in gender bias between the resulting peer reviews of groups with and without LLM suggestions.
AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot Classification
As a solution, we propose AdaSent, which decouples SEPT from DAPT by training a SEPT adapter on the base PLM.
Japanese SimCSE Technical Report
We report the development of Japanese SimCSE, Japanese sentence embedding models fine-tuned with SimCSE.
StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding
Analogy-making between narratives is crucial for human reasoning.
Sentence Embedding Models for Ancient Greek Using Multilingual Knowledge Distillation
In this work, we use a multilingual knowledge distillation approach to train BERT models to produce sentence embeddings for Ancient Greek text.
SONAR: Sentence-Level Multimodal and Language-Agnostic Representations
Our single text encoder, covering 200 languages, substantially outperforms existing sentence embeddings such as LASER3 and LabSE on the xsim and xsim++ multilingual similarity search tasks.
Scaling Sentence Embeddings with Large Language Models
We also fine-tune LLMs with current contrastive learning approach, and the 2. 7B OPT model, incorporating our prompt-based method, surpasses the performance of 4. 8B ST5, achieving the new state-of-the-art results on STS tasks.
Whitening-based Contrastive Learning of Sentence Embeddings
Consequently, using multiple positive samples with enhanced diversity further improves contrastive learning due to better alignment.
Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning via Compositional Operations
It is unclear whether the compositional semantics of sentences can be directly reflected as compositional operations in the embedding space.
Natural Language Decompositions of Implicit Content Enable Better Text Representations
When people interpret text, they rely on inferences that go beyond the observed language itself.