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.
arXiv preprint arXiv:2209.06869 , year=
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LLMs show mixed results on authorship verification, post generation, and attribute inference from Twitter data, with new frameworks and user studies establishing benchmarks for these analytics tasks.
Short LLM prompts contain distinctive lexical signals enabling user identification as a behavioral biometric, with lexical features outperforming semantic ones across a dataset of 20k+ prompts from 1k users.
citing papers explorer
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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.
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Assessing Capabilities of Large Language Models in Social Media Analytics: A Multi-task Quest
LLMs show mixed results on authorship verification, post generation, and attribute inference from Twitter data, with new frameworks and user studies establishing benchmarks for these analytics tasks.
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PromptPrint: Behavioral Biometrics Through Natural Language Prompting in LLMs
Short LLM prompts contain distinctive lexical signals enabling user identification as a behavioral biometric, with lexical features outperforming semantic ones across a dataset of 20k+ prompts from 1k users.