Large language models (LLMs) that have been optimized through human feedback have rapidly emerged as a leading paradigm for developing intelligent conversational agents. However, despite their strong performance across many benchmarks, LLM-based agents can still lack multi-turn conversational skills such as disambiguation — when they are faced with ambiguity, they often overhedge or implicitly guess users' true intents rather than asking clarifying questions. Yet high-quality conversation s...| research.google
In this blog post you will learn how to evaluate LLMs using Hugging Face lighteval on Amazon SageMaker.| www.philschmid.de
We’re on a journey to advance and democratize artificial intelligence through open source and open science.| huggingface.co
Direct (DPO) vs. RL methods for preferences, more RLHF models, and hard truths in open RLHF work. We have more questions than answers.| www.interconnects.ai