A decoder is trained on 1010 style features to map style representations back to prompts, outperforming direct LLM prompting on style recovery, imitation, and steering tasks.
Findings of the Association for Computational Linguistics: EMNLP 2024 , year =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Embeddings reliably capture authorial stylistic features in French literary texts, and these signals persist after LLM rewriting while showing model-specific patterns.
citing papers explorer
-
Interpreting Style Representations via Style-Eliciting Prompts
A decoder is trained on 1010 style features to map style representations back to prompts, outperforming direct LLM prompting on style recovery, imitation, and steering tasks.
-
Measuring Embedding Sensitivity to Authorial Style in French: Comparing Literary Texts with Language Model Rewritings
Embeddings reliably capture authorial stylistic features in French literary texts, and these signals persist after LLM rewriting while showing model-specific patterns.