Search Results for author:
Found 7 papers, 4 papers with code
Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMs
In this paper, we introduce a black-box prompt optimization method that uses an attacker LLM agent to uncover higher levels of memorization in a victim agent, compared to what is revealed by prompting the target model with the training data directly, which is the dominant approach of quantifying memorization in LLMs.
Do Membership Inference Attacks Work on Large Language Models?
Membership inference attacks (MIAs) attempt to predict whether a particular datapoint is a member of a target model's training data.
A Roadmap to Pluralistic Alignment
We identify and formalize three possible ways to define and operationalize pluralism in AI systems: 1) Overton pluralistic models that present a spectrum of reasonable responses; 2) Steerably pluralistic models that can steer to reflect certain perspectives; and 3) Distributionally pluralistic models that are well-calibrated to a given population in distribution.
A Block Metropolis-Hastings Sampler for Controllable Energy-based Text Generation
Recent work has shown that energy-based language modeling is an effective framework for controllable text generation because it enables flexible integration of arbitrary discriminators.
Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory
The interactive use of large language models (LLMs) in AI assistants (at work, home, etc.)
Misusing Tools in Large Language Models With Visual Adversarial Examples
Large Language Models (LLMs) are being enhanced with the ability to use tools and to process multiple modalities.
Smaller Language Models are Better Black-box Machine-Generated Text Detectors
With the advent of fluent generative language models that can produce convincing utterances very similar to those written by humans, distinguishing whether a piece of text is machine-generated or human-written becomes more challenging and more important, as such models could be used to spread misinformation, fake news, fake reviews and to mimic certain authors and figures.
