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Gaurav Kamath

@grvkamath.bsky.social

40 followers 45 following 24 posts

PhD-ing at McGill Linguistics + Mila, working under Prof. Siva Reddy. Mostly computational linguistics, with some NLP; habitually disappointed Arsenal fan

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Gaurav Kamath @grvkamath.bsky.social · Jul 29

Our new paper in #PNAS (bit.ly/4fcWfma) presents a surprising finding—when words change meaning, older speakers rapidly adopt the new usage; inter-generational differences are often minor.

w/ Michelle Yang, ‪@sivareddyg.bsky.social‬ , @msonderegger.bsky.social‬ and @dallascard.bsky.social‬👇(1/12)

Reposted by Gaurav Kamath

Tom McCoy @rtommccoy.bsky.social · Aug 15

🤖 🧠 NEW PAPER ON COGSCI & AI 🧠 🤖

Recent neural networks capture properties long thought to require symbols: compositionality, productivity, rapid learning

So what role should symbols play in theories of the mind? For our answer...read on!

Paper: arxiv.org/abs/2508.05776

1/n

The top shows the title and authors of the paper: "Whither symbols in the era of advanced neural networks?" by Tom Griffiths, Brenden Lake, Tom McCoy, Ellie Pavlick, and Taylor Webb.

At the bottom is text saying "Modern neural networks display capacities traditionally believed to require symbolic systems. This motivates a re-assessment of the role of symbols in cognitive theories."

In the middle is a graphic illustrating this text by showing three capacities: compositionality, productivity, and inductive biases. For each one, there is an illustration of a neural network displaying it. For compositionality, the illustration is DALL-E 3 creating an image of a teddy bear skateboarding in Times Square. For productivity, the illustration is novel words produced by GPT-2: "IKEA-ness", "nonneotropical", "Brazilianisms", "quackdom", "Smurfverse". For inductive biases, the illustration is a graph showing that a meta-learned neural network can learn formal languages from a small number of examples.

Reposted by Gaurav Kamath

Proceedings of the National Academy of Sciences @pnas.org · Aug 11

Using congressional speeches as a corpus, researchers quantify how younger and older adults adopt new meanings for words as language changes. Older people may be a bit slower to change, but can show considerable linguistic flexibility. In PNAS: www.pnas.org/doi/10.1073/...

Examples of word sense probability over the time range of the corpus.

Reposted by Gaurav Kamath

Philip Ball @philipcball.bsky.social · Jul 30

My latest column for @thenewworldmag.bsky.social looks at the question of how new meanings for words spread in the population.
www.thenewworld.co.uk/philip-ball-...

Why we need to be more chill about language change

It appears that our vocabulary is entrained with the Zeitgeist, whether we like it or not

www.thenewworld.co.uk

Gaurav Kamath @grvkamath.bsky.social · Jul 30

What's most likely is that this IS a factor for a portion of our more recent data, but not enough to affect the main finding here (across a range of words and decades). Tyvm for the interest in this!!

Cool article that's relevant: www.newyorker.com/magazine/200...

Gaurav Kamath @grvkamath.bsky.social · Jul 30

Very valid q! It's likely a confound for some of the more recent data, but not most. (i) lots of the "speeches" are in fact shorter replies and remarks; (ii) the professionalization of speech-writing evolved over the 20th century, but we see no change in speakers' adoption behavior over time.

Gaurav Kamath @grvkamath.bsky.social · Jul 30

tysm, means a lot coming from you!

Gaurav Kamath @grvkamath.bsky.social · Jul 29

Ultimately, we hope the insights from this work spur more work that uses tools from NLP to answer questions about human language.

Massive thanks to co-auths: Michelle Yang, ‪@sivareddyg.bsky.social‬, @msonderegger.bsky.social‬ and @dallascard.bsky.social‬!

Paper: bit.ly/4fcWfma. (12/12)

Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...

Gaurav Kamath @grvkamath.bsky.social · Jul 29

Limitations: Congressional speech is time-annotated linguistic data, from thousands of speakers whose ages are known, across over a century—rare, required properties for this study. But Congress was and is not socially representative. Plus: what about other languages and societies? (11/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

…while at a methodological level, they suggest that sociolinguists should avoid relying too much on apparent time differences—i.e. using older speakers as a window into the past—to identify ongoing semantic shifts. (10/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

Our findings have both conceptual and methodological implications. At a conceptual level, they suggest that the social dynamics of word meaning change are generation-agnostic, and that speakers are capable of adapting their lexicon well into adulthood (unlike, e.g., their phonology)... (9/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

These findings extend to the level of the individual: members of Congress that gave speeches over a long enough period of time showed significant changes in how they used some of our target words, mimicking population-level trends in word meaning change. (8/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

Overall, we find that age has very little effect—older speakers lag slightly behind younger ones, but match their word usage within just a few years; in some cases, they even lead change. Semantic change appears driven almost purely by time, with only minor inter-generational differences. (7/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

Finally, we use Generalized Additive Mixed-effect Models (GAMMs) to model the likelihood of a word being used in a specific sense, given the year of its use and a speaker’s age at the time, while accounting for other inter-speaker variation. (6/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

We identify >100 words suspected to have undergone meaning change in our corpus. We then use a Masked Language Model to induce several distinct, interpretable senses of each of these words, by clustering the MLM’s substitution predictions for the target word given different usage contexts. (5/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

To answer these questions, we conduct the first large-scale investigation of semantic change across both time and speaker age. We look at ~7.9M speeches from the U.S. Congress from 1873-2010, to ask whether semantic changes are led only by specific generations, or if everyone joins in. (4/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

Example: “workshop” used to solely refer to a physical place of work; now it refers to a type of conference/seminar. As this change occurred, did older speakers learn to use “workshop” in its newer sense? Or did the dominant meaning of the word change only because those generations died out? (3/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

A major question in linguistics is how languages evolve within our lifetimes. Change could be purely down to inter-generational turnover; or it could involve people of old and new generations alike participating in ongoing changes of language use. (2/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

Our new paper in #PNAS (bit.ly/4fcWfma) presents a surprising finding—when words change meaning, older speakers rapidly adopt the new usage; inter-generational differences are often minor.

w/ Michelle Yang, ‪@sivareddyg.bsky.social‬ , @msonderegger.bsky.social‬ and @dallascard.bsky.social‬👇(1/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

…while at a methodological level, they suggest that sociolinguists should avoid relying too much on "apparent time" differences—i.e. using older speakers as a window into the past—to identify ongoing semantic shifts. (10/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

Our findings have both conceptual and methodological implications. At a conceptual level, they suggest that the social dynamics of word meaning change are generation-agnostic, and that speakers are capable of adapting their lexicon well into adulthood (unlike, e.g., their phonology)... (9/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

These findings extend to the level of the individual: members of Congress that gave speeches over a long enough period of time showed significant changes in how they used some of our target words, mimicking population-level trends in word meaning change. (8/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

Overall, we find that age has very little effect—older speakers lag slightly behind younger ones, but match their word usage within just a few years; in some cases, they even lead change. Semantic change appears driven almost purely by time, with only minor inter-generational differences. (7/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

Finally, we use Generalized Additive Mixed-effect Models (GAMMs) to model the likelihood of a word being used in a specific sense, given the year of its use and a speaker’s age at the time, while accounting for other inter-speaker variation. (6/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

We identify >100 words suspected to have undergone meaning change in our corpus. We then use a Masked Language Model to induce several distinct, interpretable senses of each of these words, by clustering the MLM’s substitution predictions for the target word given different usage contexts. (5/12)

Gaurav Kamath @grvkamath.bsky.social · Jul 29

To answer these questions, we conduct the first large-scale investigation of semantic change across both time and speaker age. We look at ~7.9M speeches from the U.S. Congress from 1873-2010, to ask whether semantic changes are led only by specific generations, or if everyone joins in. (4/12)