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"id": "https://tldr.nettime.org/users/remixtures/statuses/113316373187402241",
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"published": "2024-10-16T09:23:55Z",
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"content": "<p><a href=\"https://tldr.nettime.org/tags/AI\" class=\"mention hashtag\" rel=\"tag\">#<span>AI</span></a> <a href=\"https://tldr.nettime.org/tags/GenerativeAI\" class=\"mention hashtag\" rel=\"tag\">#<span>GenerativeAI</span></a> <a href=\"https://tldr.nettime.org/tags/LLMs\" class=\"mention hashtag\" rel=\"tag\">#<span>LLMs</span></a> <a href=\"https://tldr.nettime.org/tags/Reasoning\" class=\"mention hashtag\" rel=\"tag\">#<span>Reasoning</span></a>: "The tested LLMs fared much worse, though, when the Apple researchers modified the GSM-Symbolic benchmark by adding "seemingly relevant but ultimately inconsequential statements" to the questions. For this "GSM-NoOp" benchmark set (short for "no operation"), a question about how many kiwis someone picks across multiple days might be modified to include the incidental detail that "five of them [the kiwis] were a bit smaller than average."</p><p>Adding in these red herrings led to what the researchers termed "catastrophic performance drops" in accuracy compared to GSM8K, ranging from 17.5 percent to a whopping 65.7 percent, depending on the model tested. These massive drops in accuracy highlight the inherent limits in using simple "pattern matching" to "convert statements to operations without truly understanding their meaning," the researchers write.</p><p>In the example with the smaller kiwis, for instance, most models try to subtract the smaller fruits from the final total because, the researchers surmise, "their training datasets included similar examples that required conversion to subtraction operations." This is the kind of "critical flaw" that the researchers say "suggests deeper issues in [the models'] reasoning processes" that can't be helped with fine-tuning or other refinements."</p><p><a href=\"https://www.wired.com/story/apple-ai-llm-reasoning-research/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://www.</span><span class=\"ellipsis\">wired.com/story/apple-ai-llm-r</span><span class=\"invisible\">easoning-research/</span></a></p>",
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"pt": "<p><a href=\"https://tldr.nettime.org/tags/AI\" class=\"mention hashtag\" rel=\"tag\">#<span>AI</span></a> <a href=\"https://tldr.nettime.org/tags/GenerativeAI\" class=\"mention hashtag\" rel=\"tag\">#<span>GenerativeAI</span></a> <a href=\"https://tldr.nettime.org/tags/LLMs\" class=\"mention hashtag\" rel=\"tag\">#<span>LLMs</span></a> <a href=\"https://tldr.nettime.org/tags/Reasoning\" class=\"mention hashtag\" rel=\"tag\">#<span>Reasoning</span></a>: "The tested LLMs fared much worse, though, when the Apple researchers modified the GSM-Symbolic benchmark by adding "seemingly relevant but ultimately inconsequential statements" to the questions. For this "GSM-NoOp" benchmark set (short for "no operation"), a question about how many kiwis someone picks across multiple days might be modified to include the incidental detail that "five of them [the kiwis] were a bit smaller than average."</p><p>Adding in these red herrings led to what the researchers termed "catastrophic performance drops" in accuracy compared to GSM8K, ranging from 17.5 percent to a whopping 65.7 percent, depending on the model tested. These massive drops in accuracy highlight the inherent limits in using simple "pattern matching" to "convert statements to operations without truly understanding their meaning," the researchers write.</p><p>In the example with the smaller kiwis, for instance, most models try to subtract the smaller fruits from the final total because, the researchers surmise, "their training datasets included similar examples that required conversion to subtraction operations." This is the kind of "critical flaw" that the researchers say "suggests deeper issues in [the models'] reasoning processes" that can't be helped with fine-tuning or other refinements."</p><p><a href=\"https://www.wired.com/story/apple-ai-llm-reasoning-research/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://www.</span><span class=\"ellipsis\">wired.com/story/apple-ai-llm-r</span><span class=\"invisible\">easoning-research/</span></a></p>"
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