Skill UI

Speeds up LLM inference by combining speculative decoding with Medusa multiple heads and lookahead techniques so chatbots, code assistants, and other real-time workloads can run with 1.5-3.6× lower latency while preserving target-model quality.

A comprehensive guide detailing best practices for designing and auditing empirical evaluations of Large Language Models (LLMs). It addresses critical pitfalls such as data contamination, baseline unfairness, model version drift, and decoding parameter sensitivity, ensuring that published results are robust, reproducible, and scientifically sound for academic publication.

This comprehensive guide helps researchers position their work in Automatic Speech Recognition (ASR) and Spoken Language Understanding (SLU) for the IEEE ASRU workshop. It covers conference fit assessment, manuscript re-framing strategies, understanding venue-specific evidence requirements (e.g., decoding, robustness analysis), and differentiating the contribution from related fields like Interspeech or SLT. Use this tool for submission strategy and academic positioning.