Summary of Logits Of Api-protected Llms Leak Proprietary Information, by Matthew Finlayson et al.
Logits of API-Protected LLMs Leak Proprietary Information
by Matthew Finlayson, Xiang Ren, Swabha Swayamdipta
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper reveals that despite Large Language Model (LLM) providers restricting access to their models through proprietary APIs, it’s possible to learn significant information about the model from just a few API queries. The researchers found that most modern LLMs are restricted by a “softmax bottleneck,” which limits their output to a specific subspace. They exploited this limitation to unlock various capabilities, including obtaining full-vocabulary outputs, auditing model updates, identifying the source LLM, and discovering the hidden size. Their experiments showed the effectiveness of these methods, allowing them to estimate the embedding size of OpenAI’s gpt-3.5-turbo to be approximately 4096. The paper discusses potential countermeasures for LLM providers and highlights the benefits of increased transparency and accountability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that even with limited access to a Large Language Model (LLM), it’s possible to learn important details about how the model works. By studying how the model responds to different inputs, researchers found that they could figure out some key features of the model without having to see its internal workings. This is important because it means that we can get more information about these powerful models without having to rely on the companies that create them. The researchers even used this technique to discover some secrets about a popular LLM called gpt-3.5-turbo. Overall, this study shows that there are ways to increase transparency and accountability in the use of these powerful AI tools. |
Keywords
* Artificial intelligence * Embedding * Gpt * Large language model * Softmax
