arXiv (All)
open access
ALAS: An Automatic Latent Alignment Score for Audio Language Models
Large Language Models (LLMs) are extended into Speech-LLMs, and the quality of the audio--text alignment they learn affects most downstream Spoken Language Understanding (SLU) behavior. Yet despite a growth of fusion strategies, there is no standard way to measure how well a Speech-LLM internally bi
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
- Aligned but Flattened: Analyzing the Trade-off between Cultural Alignment and Diversity in LLMs#820004
- MusTBench: Benchmarking and Advancing Temporal Grounding in Music LLMs#821455
- TEMPO: Temporally-grounded Multi-task Post-training for Large Audio-Language Models#799245
- TEMPO: Temporally-grounded Multi-task Post-training for Large Audio-Language Models#797822
- Training-Free Speech-Centric Omni Understanding with Frozen VLMs#927543
Record · ID 1117230
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