MemGuard-Alpha: Limits of Membership Inference for Detecting and Filtering Memorization-Contaminated Signals in LLM-Based Financial Forecasting
Large language models are increasingly used to generate financial alpha signals, but many have memorized the historical data in their training corpora, producing apparent accuracy that collapses out of sample. Membership inference attacks (MIA) have been proposed as a diagnostic. What has not been established is whether MIA scores are informative about memorization in this setting, or whether signal-level filtering built on them helps once realistic costs are applied.We introduce MemGuard-Alpha, comprising a composite contamination score combining five MIA methods with a temporal proximity feature, and Cross-Model Memorization Disagreement, which exploits variation in training cutoffs acr...
| Authors | Anisha Roy, Dip Roy |
|---|---|
| Primary category | cs.LG |
| Published | 26 March 2026 |
| Last updated at source | 24 September 2026 |
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