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SUMMARY:Fundamentals of AI Reading Group: LLM and Human Modes of Representation

DESCRIPTION;ENCODING=QUOTED-PRINTABLE: LLM and Human Modes of Representation=0D=0A=
Shalom Lappin=0D=0A=
Queen Mary University of London, University of Gothenburg, and King's College London=0D=0A=
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Much work on the cognitive foundations of AI has focussed on comparisons between the=0D=0A=
ways in which Large Language Models (LLMs) and humans process information and represent=0D=0A=
it. One aspect of this comparison involves determining the extent to which LLMs can achieve=0D=0A=
or surpass human performance on a variety of cognitively interesting tasks. A second explores=0D=0A=
points of convergence and divergence between LLM and human systems for processing information.=0D=0A=
Here, I consider some recent research that has addressed both issues in two informational=0D=0A=
domains. The first is the representation of linguistic knowledge. The second is real world reason-=0D=0A=
ing and planning. While LLMs frequently achieve impressive levels of performance and fluency=0D=0A=
on linguistic applications, they tend to handle linguistic content in ways that are distinct from=0D=0A=
human processing. They are also, for the most part, less efficient than humans in learning and=0D=0A=
generalisation for reasoning tasks.=0D=0A=
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LOCATION:QMUL Engineering Building, G2. E1 4NS
DTSTART:20260513T103000
DTEND:20260513T130000
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