Despite their wide use, large language models are still mysterious. Revealing their true nature is urgent
). For LLMs to solve problems and increase productivity in fields such as medicine and law, people need to better understand both the successes and failures of these tools. This will require new tests that offer a more systematic assessment than those that exist today.LLMs ingest enormous reams of text, which they use to learn to predict the next word in a sentence or conversation.
Scientists are now debating what is going on under the hood of LLMs, given this mixed performance. On one side are researchers who see glimmers of reasoning and understanding when the models succeed at some tests. On the other are those who see their unreliability as a sign that the model is not as smart as it seems.More systematic tests of LLMs’ capabilities would help to settle the debate. These would provide a more robust understanding of the models’ strengths and weaknesses.
In May, a team of researchers led by Melanie Mitchell, a computer scientist at the Santa Fe Institute in New Mexico, reported the creation of ConceptARC (
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