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Arxiv
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GPT, But Backwards: Exactly Inverting Language Model Outputs
- A new technique has been developed to reconstruct the exact input that led to a language model's output, aiding in post-incident analysis and fake output detection.
- The technique, called SODA, is a gradient-based algorithm that outperforms existing methods in recovering shorter out-of-distribution inputs from language models.
- The experiments conducted on LLMs ranging from 33M to 3B parameters showed that SODA was successful in fully recovering 79.5% of shorter inputs but faced challenges with longer input sequences.
- The study suggests that standard deployment practices may currently offer sufficient protection against the potential misuse of this reconstructive method.
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