Papers by Guillem Ramírez
Cache & Distil: Optimising API Calls to Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models are expensive to run and expose the entire request stream to external providers. |
| Approach: | They propose to locally train a small private language model on the LLM's predictions to minimise the costs and data exposure associated with calling the API. |
| Outcome: | The proposed model can handle an increasing number of user requests independently and is able to perform better than other policies and baselines across tasks and budgets. |
Controlling What You Share: Assessing Language Model Adherence to Privacy Preferences (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are accessed via commercial APIs, but expose data to service providers. |
| Approach: | They propose a framework where a local model uses natural language instructions to rewrite queries and paired them with synthetic privacy profiles to achieve better privacy preservation. |
| Outcome: | The proposed model outperforms large-scale few-shot models in terms of privacy preservation and performance. |