Today we’re joined by Anna Ivanova, a postdoctoral researcher at MIT Quest for Intelligence. In our conversation with Anna, we discuss her recent paper Dissociating language and thought in large language models: a cognitive perspective. In the paper, Anna reviews the capabilities of LLMs by considering their performance on two different aspects of language use: 'formal linguistic competence', which includes knowledge of rules and patterns of a given language, and 'functional linguistic competence', a host of cognitive abilities required for language understanding and use in the real world. We explore parallels between linguistic competence and AGI, the need to identify new benchmarks for these models, whether an end-to-end trained LLM can address various aspects of functional competence, and much more!
The complete show notes for this episode can be found at twimlai.com/go/620.
GraphRAG: Knowledge Graphs for AI Applications with Kirk Marple - #681
Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - #680
Localizing and Editing Knowledge in LLMs with Peter Hase - #679
Coercing LLMs to Do and Reveal (Almost) Anything with Jonas Geiping - #678
V-JEPA, AI Reasoning from a Non-Generative Architecture with Mido Assran - #677
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Are Vector DBs the Future Data Platform for AI? with Ed Anuff - #664
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Responsible AI in the Generative Era with Michael Kearns - #662
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