  Upon its release in late 2022, ChatGPT has brought a seismic shift in the
entire landscape of AI, both in research and commerce. Through
instruction-tuning a large language model (LLM) with supervised fine-tuning and
reinforcement learning from human feedback, it showed that a model could answer
human questions and follow instructions on a broad panel of tasks. Following
this success, interests in LLMs have intensified, with new LLMs flourishing at
frequent interval across academia and industry, including many start-ups
focused on LLMs. While closed-source LLMs (e.g., OpenAI's GPT, Anthropic's
Claude) generally outperform their open-source counterparts, the progress on
the latter has been rapid with claims of achieving parity or even better on
certain tasks. This has crucial implications not only on research but also on
business. In this work, on the first anniversary of ChatGPT, we provide an
exhaustive overview of this success, surveying all tasks where an open-source
LLM has claimed to be on par or better than ChatGPT.
