Papers with style embeddings

2 papers
mStyleDistance: Multilingual Style Embeddings and their Evaluation (2025.findings-acl)

Copied to clipboard

Challenge: Multilingual StyleDistance embeddings are useful for stylistic analysis and style transfer, but they only exist for English.
Approach: They propose a method that can generate style embeddings in new languages using synthetic data and a contrastive loss.
Outcome: The proposed method outperforms existing style embeddings on these benchmarks and generalizes well to unseen features and languages.
Linguistic and Embedding-Based Profiling of Texts Generated by Humans and Large Language Models (2025.emnlp-main)

Copied to clipboard

Challenge: Recent studies have focused on using LLMs to classify text as either human-written or machine-generated .
Approach: They characterize human-written and machine-generated texts using a set of linguistic features across different linguistic levels such as morphology, syntax, and semantics.
Outcome: The proposed model reveals that human-written texts exhibit simpler syntactic structures and more diverse semantic content.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations