Seminars and Events
NL Seminar- How to Steal ChatGPT’s Embedding Size, and Other Low-rank Logit Tricks
Event Details
Speaker: Matt Finlayson, USC
Conference Rm Location: ISI-MDR #689
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Abstract:
The commercialization of large language models (LLMs) has led to the common practice of restricting access to proprietary models via a limited API. In this work we show that, with only a conservative assumption about the model architecture, it is possible to learn a surprisingly large amount of non-public information about an API-protected LLM from a relatively small number of API queries (e.g., costing under $1000 USD for OpenAI’s gpt-3.5-turbo). Our findings are centered on one key observation: most modern LLMs suffer from a softmax bottleneck, which restricts the model outputs to a linear subspace of the full output space. We exploit this fact to unlock several capabilities, including (but not limited to) obtaining cheap full-vocabulary outputs, auditing for specific types of model updates, identifying the source LLM given a single full LLM output, and even efficiently discovering the LLM’s hidden size. Our empirical investigations show the effectiveness of our methods, which allow us to estimate the embedding size of OpenAI’s gpt-3.5-turbo to be about 4096. Lastly, we discuss ways that LLM providers can guard against these attacks, as well as how these capabilities can be viewed as a feature (rather than a bug) by allowing for greater transparency and accountability.
Speaker Bio
Matthew Finlayson is a PhD student studying NLP at the University of Southern California. Previously he was a predoctoral researcher at the Allen Institute for AI (AI2) after completing his bachelors degree in computer science and linguistics at Harvard University. Matthew is interested in the practical consequences of the architectural design of language models, from security to generation, as well as understanding how language models learn and generalize from data.
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