Researchers propose rank-aware method for private listwise LLM alignment
Existing methods focus on pairwise comparisons, but listwise feedback—where full rankings are provided—reveals more sensitive user data. The paper highlights that applying local differential privacy (LDP) to listwise data requires excessive noise, which weakens the benefits of richer supervision.
Key points
- New rank-aware exponential mechanism privatizes listwise preference data for LLM alignment
- Method reduces noise by focusing on borderline items, not uniform perturbation
- Empirical tests show better performance than existing LDP baselines at matched privacy levels
The authors propose a rank-aware exponential mechanism that privatizes listwise preferences by sampling a fixed-size binary partition. This reduces noise concentration near borderline items instead of uniformly perturbing all items, as traditional randomized response does. Empirical tests show their method outperforms existing LDP baselines at equivalent privacy levels. The code is available on GitHub at https://github.com/tdsai-lab/RAEM.
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