A new research paper explores the potential for information leakage from large language models, even at the final logit level. Researchers used vision-language models to compare information retained at different representational stages, from the full residual stream to compressed bottlenecks like top-k logits. The study found that easily accessible bottlenecks, such as the model's top logit values, can inadvertently reveal task-irrelevant information from an image-based query, sometimes as much as direct projections of the entire residual stream. AI
IMPACT Highlights potential security risks in LLMs, suggesting that even final output probabilities could leak sensitive data.
RANK_REASON The cluster contains a research paper published on arXiv detailing findings about information leakage in AI models. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- Influence Flower
- Masha Fedzechkina
- ScienceCast
- top-k logits
- vision-language models
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