AI

DiRAFT: Privacy-Aware Financial Transcript Synthesis from Aggregate Discourse Priors

Read full article at source ↗

Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 02/11/2026, 05:03:25 UTC.

Briefings

DiRAFT is a privacy-aware financial transcript synthesis method that uses aggregate discourse priors to generate synthetic financial transcripts. It extracts concept graphs from investor calls and records the order of concept introduction, using stochastic sampling to balance fidelity and structural overlap. The system evaluates privacy risks through corpus-level overlap checks and human review of 1,400 investor-call outputs across seven strategies and two backends. Results show stochastic sampling detects fewer confidential fact matches than greedy or top-3 methods, offering empirical evidence on residual confidentiality risks without formal guarantees.

Key points

• DiRAFT uses stochastic sampling to generate synthetic financial transcripts from aggregate discourse priors.
• The system evaluates privacy risks by comparing transition fidelity against greedy and top-3 sampling methods.

Context

Financial language models trained on customer conversations face confidentiality risks due to non-public facts remaining after identifier masking. DiRAFT addresses this by extracting concept graphs and recording concept introduction order, using stochastic sampling to balance fidelity and overlap.

Limitations

Confidentiality assessment relies on human review of 1,400 investor-call outputs across seven strategies and two backends. No formal privacy guarantee is established; stochastic sampling provides empirical evidence on residual risks without formal verification.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 02/11/2026, 06:00:52 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

View archived briefing on IPFS
bafybeibumchwzgcws7dm3ii4mkex6iidbv5qruzmgrueedhgnifu63sabe