arXiv (OAI Expanded)
open access
Machine Unlearning for Speech Question Answering in Large Audio-Language Models
Large Audio-Language Models (LALMs) have recently shown strong capabilities in speech understanding and question answering (QA), but they also inherit privacy risks from large-scale training data, including the unintended memorization of sensitive information. In this work, we study machine unlearni
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SPEAR-Gen: Generation-Aware Pre-training for Unified Speech Representations#1119220
- Language Orthogonalization for Zero-Shot Cross-Lingual Audio Deepfake Detection#1122503
- SURE-Voice: A Front-End Baseline for Speech-Evidence Filtering in Speech LLMs#1084345
- Evaluating Machine Unlearning in ASR#1119165
- MLAAD: The Multi-Language Audio Anti-Spoofing Dataset#785033
Record · ID 1071003
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive