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<p class="MsoNormal"><span lang="EN-US">Hi all,</span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Next week (2/8) we will be discussing “Dissociating language and thought in large language models” (Mahowald et al., 2023).</span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span lang="EN-US">Abstract</span></b><span lang="EN-US">:</span></p>
<p class="MsoNormal"><span lang="EN-US">Large language models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split. Here, we evaluate LLMs using a distinction
between formal linguistic competence knowledge of linguistic rules and patterns — and functional linguistic competence — understanding and using language in the world. We ground this distinction in human neuroscience, showing that formal and functional competence
rely on different neural mechanisms. Although LLMs are surprisingly good at formal competence, their performance on functional competence tasks remains spotty and often requires specialized fine-tuning and/or coupling with external modules. In short, LLMs
are good models of language but incomplete models of human thought.</span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span lang="EN-US">Link to paper</span></b><span lang="EN-US">:</span></p>
<p class="MsoNormal"><span lang="EN-US"><a href="https://arxiv.org/abs/2301.06627">https://arxiv.org/abs/2301.06627</a>
</span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Best,</span></p>
<p class="MsoNormal"><span lang="EN-US">Yi-Chien</span></p>
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