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

ChatGPT's One-year Anniversary: Are Open-Source Large Language Models Catching up?