Response: Emergent Analogical Reasoning In Large Language Models | AI Research Paper Details
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Response: Emergent Analogical Reasoning In Large Language Models | AI Research Paper Details
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LARGE LANGUAGE MODELS AS ANALOGICAL REASONERS | PDF | Mathematics | Algorithms
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Emergent Analogical Reasoning in Large Language Models
Emergent Analogical Reasoning in Large Language Models
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 achieve impressive performance in a wide range of tasks%2C even if they are often trained with the only objective of chatting fluently with users. Among other skills%2C LLMs show emergent abilities in mathematical reasoning benchmarks%2C which can be elicited with appropriate prompting methods. In this work%2C we systematically investigate the capabilities and limitations of popular open-source LLMs on different symbolic reasoning tasks. We evaluate three models of the Llama 2 family on two datasets that require solving mathematical formulas of varying degrees of difficulty. We test a generalist LLM (Llama 2 Chat) as well as two fine-tuned versions of Llama 2 (MAmmoTH and MetaMath) specifically designed to tackle mathematical problems. We observe that both increasing the scale of the model and fine-tuning it on relevant tasks lead to significant performance gains. Furthermore%2C using fine-grained evaluation measures%2C we find that such performance gains are mostly observed with mathematical formulas of low complexity%2C which nevertheless often remain challenging even for the largest fine-tuned models.?resize=91,91 "Assessing The Emergent Symbolic Reasoning Abilities Of Llama Large Language Models | AI Research ...")
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