Uploaded March 2023 | Updated September 2026, 13 hours ago
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Apple podcasts:
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Spotify:
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Music by: The New Year:
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Show notes:
braininspired.co/podcast/163
Ellie Pavlick runs her Language Understanding and Representation Lab at Brown University, where she studies lots of topics related to language. In AI, large language models, sometimes called foundation models, are all the rage these days, with their ability to generate convincing language, although they still make plenty of mistakes. One of the things Ellie is interested in is how these models work, what kinds of representations are being generated in them to produce the language they produce. So we discuss how she's going about studying these models. For example, probing them to see whether something symbolic-like might be implemented in the models, even though they are the deep learning neural network type, which aren't suppose to be able to work in a symbol-like manner. We also discuss whether grounding is required for language understanding - that is, whether a model that produces language well needs to connect with the real world to actually understand the text it generates. We talk about what language is for, the current limitations of large language models, how the models compare to humans, and a lot more.
0:00 - Intro
2:34 - Will LLMs make us dumb?
9:01 - Evolution of language
17:10 - Changing views on language
22:39 - Semantics, grounding, meaning
37:40 - LLMs, humans, and prediction
41:19 - How to evaluate LLMs
51:08 - Structure, semantics, and symbols in models
1:00:08 - Dimensionality
1:02:08 - Limitations of LLMs
1:07:47 - What do linguists think?
1:14:23 - What is language for?
Patreon for full episodes and Discord community:
patreon.com/braininspired
Video Series: Open Questions in AI and Neuroscience:
braininspired.co/open
Apple podcasts:
itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2
Spotify:
open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLe
Music by: The New Year:
thenewyear.net
Show notes:
braininspired.co/podcast/163
Ellie Pavlick runs her Language Understanding and Representation Lab at Brown University, where she studies lots of topics related to language. In AI, large language models, sometimes called foundation models, are all the rage these days, with their ability to generate convincing language, although they still make plenty of mistakes. One of the things Ellie is interested in is how these models work, what kinds of representations are being generated in them to produce the language they produce. So we discuss how she's going about studying these models. For example, probing them to see whether something symbolic-like might be implemented in the models, even though they are the deep learning neural network type, which aren't suppose to be able to work in a symbol-like manner. We also discuss whether grounding is required for language understanding - that is, whether a model that produces language well needs to connect with the real world to actually understand the text it generates. We talk about what language is for, the current limitations of large language models, how the models compare to humans, and a lot more.
0:00 - Intro
2:34 - Will LLMs make us dumb?
9:01 - Evolution of language
17:10 - Changing views on language
22:39 - Semantics, grounding, meaning
37:40 - LLMs, humans, and prediction
41:19 - How to evaluate LLMs
51:08 - Structure, semantics, and symbols in models
1:00:08 - Dimensionality
1:02:08 - Limitations of LLMs
1:07:47 - What do linguists think?
1:14:23 - What is language for?
