Science, Space and Technology News 2024
Researchers at Georgia Tech are advancing neural networks to mimic human decision-making by training them to exhibit variability and confidence in their choices, similar to how humans operate, as demonstrated in their study published in Nature Human Behaviour. Their model, RTNet, not only matches human performance in recognizing noisy digits but also applies human-like traits such as confidence and evidence accumulation, enhancing both accuracy and reliability. Credit: SciTechDaily.
Large language models , for example, are prone to hallucinations. When an LLM is asked a question it doesn’t know the answer to, it will make up something without acknowledging the artifice. By contrast, most humans in the same situation will admit they don’t know the answer. Building a more human-like neural network can prevent this duplicity and lead to more accurate answers.
Once they had the model’s results, they compared them to humans’ results. Sixty Georgia Tech students viewed the same dataset and shared their confidence in their decisions, and the researchers found the accuracy rate, response time, and confidence patterns were similar between the humans and the neural network.
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