Current LLM-based services typically require users to submit raw text regardless of its sensitivity.While intuitive, such practice introduces substantial privacy risks, as unauthorized access may expose personal, medical, or legal information.Although prior defenses strived to mitigate these risks, they often incur substantial computational overhead and degrade model performance.To overcome this privacy-efficiency trade-off, we introduce Privacy-Preserving Fine-Tuning (PPFT), a novel training pipeline that eliminates the need for transmitting raw prompt text while maintaining a favorable balance between privacy preservation and model utility for both clients and service providers.Our approach operates in two stages: first, we train a client-side encoder together with a server-side projection module and LLM, enabling the server to condition on k-pooled prompt embeddings instead of raw text; second, we fine-tune the projection module and LLM on private, domain-specific data using noise-injected embeddings, allowing effective adaptation without exposing plain text prompts and requiring access to the decoder's internal parameters.Extensive experiments on domainspecific and general benchmarks demonstrate that PPFT achieves a striking balance between privacy and utility, maintaining competitive performance with minimal degradation compared to noise-free upper bounds.

Large Language Models are commonly judged by their scores on standard benchmarks, yet such scores often overstate real capability since they mask the mix of skills a task actually demands.For example, ARC is assumed to test reasoning, while HellaSwag is designed to evaluate commonsense.However, we lack a systematic way to verify if these benchmarks actually measure these labels.We introduce BENCHMARK PROFILING, a diagnostic framework that decomposes benchmark performance into ten cognitively grounded abilities.The method combines gradient-based importance scoring with targeted parameter ablation to compute an Ability Impact Score (AIS) that quantifies how much each ability contributes to a model's success on a given benchmark.Profiling three instruction-tuned models across ten widely used benchmarks yields four key findings: (i) most benchmarks draw on several abilities rather than one, (ii) datasets with similar labels rely on distinct ability mixtures, (iii) code-generation benchmarks reward broad, multi-skill improvement and thus show only modest gains from narrow domain-specific fine-tuning, and (iv) abilities irrelevant to the task could negatively affect performance.BENCHMARK PROFILING therefore explains why performance gains do not always translate into user-perceived competence and offers a transparent tool for benchmark audit and model interpretability.The code is available on https://github.com/ junkim100/Benchmark-Profiling Leonard Bereska and Efstratios Gavves.