Improving LLM understanding of structured data and exploring advanced prompting methods
Structural Understanding Capabilities is a new benchmark for evaluating and improving LLM comprehension of structured table data. This advance can help LLMs process and analyze data more effectively, broadening their applicability in real-world tasks.
Orca-Math: Demonstrating the potential of SLMs with model specialization
Microsoft’s Orca-Math, a specialized small language model, outperforms much larger models in solving math problems that require multi-step reasoning and shows the potential of using feedback to improve language models. Learn more.
Research Focus: Week of February 19, 2024
In this issue: CaaSPER: vertical autoscaling algorithm dynamically maintains optimal CPU utilization; Improved scene landmark detection for camera localization runs faster, uses less storage; ESUS simplifies usability questionnaires for technical products and services.
Generative Representational Instruction Tuning
ELaTE
Making Flow-Matching-Based Zero-Shot Text-to-Speech Laugh as You Like ELaTE is a zero-shot text-to-speech (TTS) system that can generate natural laughing speech from any speaker based on a speaker prompt to mimic the voice characteristic, a…