LLM-Blender: A Simple Ensemble Learning Framework for LLMs
<p>With the rise of open-source large language models (LLMs) such as <a href="https://github.com/tatsu-lab/stanford_alpaca" rel="noopener ugc nofollow" target="_blank">Alpaca</a>, <a href="https://lmsys.org/blog/2023-03-30-vicuna/" rel="noopener ugc nofollow" target="_blank">Vicuna</a>, and <a href="https://falconllm.tii.ae/?gclid=CjwKCAjw5MOlBhBTEiwAAJ8e1mt0_CXTsf-I28V2vU2rMnm3eBxb40PKnFZbE-K_cl5jcuJi-eJZjRoChGUQAvD_BwE" rel="noopener ugc nofollow" target="_blank">Falcon</a>, we are witnessing boundary-pushing possibilities in this realm. Certain models demonstrate superior overall performances on leaderboards like <a href="https://tatsu-lab.github.io/alpaca_eval/" rel="noopener ugc nofollow" target="_blank">AlpacaEval</a> and <a href="https://lmsys.org/blog/2023-05-03-arena/" rel="noopener ugc nofollow" target="_blank">Chatbot Arena</a>. <strong>However, is it reasonable to stick with one top-performing LLM for all user inputs? </strong>The answer may not be as straightforward as one might think.</p>
<p><a href="https://medium.com/ai2-blog/llm-blender-a-simple-ensemble-learning-framework-for-llms-9e4bc57af23e"><strong>Read more</strong></a></p>
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