SICL-AT: Another way to adapt Auditory LLM to low-resource task
SICL-AT: Another way to adapt Auditory LLM to low-resource task explores Enhance auditory LLMs for low-resource tasks using Speech In-Context Learning Adaptation Training.. Commercial viability score: 5/10 in Auditory AI.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisTraining In-Context and In-Weights Mixtures Via Contrastive Context SamplingViability: 3.0
- Higher ViabilityHIFICL: High-Fidelity In-Context Learning for Multimodal TasksViability: 8.0
- Higher ViabilityParallel In-context Learning for Large Vision Language ModelsViability: 7.0
- Higher ViabilityAdaptive Discovery of Interpretable Audio Attributes with Multimodal LLMs for Low-Resource ClassificationViability: 7.0
- Higher ViabilityUniICL: Systematizing Unified Multimodal In-context Learning through a Capability-Oriented Taxonomy