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Reposted by Kanishka Misra 🌊

Linguischick @cocchino.bsky.social · 9h

We're hiring! UT Linguistics invites applications for a position in computational linguistics to begin next academic year 2026-27 (rank of tenure-track Assistant Professor or Associate Professor with tenure). apply.interfolio.com/175156

Apply - Interfolio {{$ctrl.$state.data.pageTitle}} - Apply - Interfolio

apply.interfolio.com

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Kanishka Misra 🌊 @kanishka.bsky.social · 12h

Happening now! Poster 42!

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Kanishka Misra 🌊 @kanishka.bsky.social · 13h

Catch Qing’s poster in the morning poster session today!!

I’ll also be there, talk to me about UT Ling’s new comp ling job/methods to study linguistic generalization/and how LMs *might* inform language science!

Qing Yao @qyao.bsky.social · Mar 31

LMs learn argument-based preferences for dative constructions (preferring recipient first when it’s shorter), consistent with humans. Is this from memorizing preferences in training? New paper w/ @kanishka.bsky.social , @weissweiler.bsky.social , @kmahowald.bsky.social

arxiv.org/abs/2503.20850

examples from direct and prepositional object datives with short-first and long-first word orders:
DO (long first): She gave the boy who signed up for class and was excited it.
PO (short first): She gave it to the boy who signed up for class and was excited.
DO (short first): She gave him the book that everyone was excited to read.
PO (long-first): She gave the book that everyone was excited to read to him.

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Reposted by Kanishka Misra 🌊

Kyle Mahowald (COLM 2025) @kmahowald.bsky.social · 1d

UT Austin Linguistics is hiring in computational linguistics!

Asst or Assoc.

We have a thriving group sites.utexas.edu/compling/ and a long proud history in the space. (For instance, fun fact, Jeff Elman was a UT Austin Linguistics Ph.D.)

faculty.utexas.edu/career/170793

🤘

UT Austin Computational Linguistics Research Group – Humans processing computers processing humans processing language

sites.utexas.edu

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Kanishka Misra 🌊 @kanishka.bsky.social · 1d

Come join us at the city of ACL!

Very happy to chat about my experience as a new faculty at UT Ling, come find me at #COLM2025 if you’re interested!!

Kyle Mahowald (COLM 2025) @kmahowald.bsky.social · 1d

UT Austin Linguistics is hiring in computational linguistics!

Asst or Assoc.

We have a thriving group sites.utexas.edu/compling/ and a long proud history in the space. (For instance, fun fact, Jeff Elman was a UT Austin Linguistics Ph.D.)

faculty.utexas.edu/career/170793

🤘

UT Austin Computational Linguistics Research Group – Humans processing computers processing humans processing language

sites.utexas.edu

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Reposted by Kanishka Misra 🌊

Sasha Boguraev @ COLM @sashaboguraev.bsky.social · 2d

I will be giving a short talk on this work at the COLM Interplay workshop on Friday (also to appear at EMNLP)!

Will be in Montreal all week and excited to chat about LM interpretability + its interaction with human cognition and ling theory.

Sasha Boguraev @ COLM @sashaboguraev.bsky.social · May 27

A key hypothesis in the history of linguistics is that different constructions share underlying structure. We take advantage of recent advances in mechanistic interpretability to test this hypothesis in Language Models.

New work with @kmahowald.bsky.social and @cgpotts.bsky.social!

🧵👇!

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Reposted by Kanishka Misra 🌊

Jessy Li @jessyjli.bsky.social · 2d

On my way to #COLM2025 🍁

Check out jessyli.com/colm2025

QUDsim: Discourse templates in LLM stories arxiv.org/abs/2504.09373

EvalAgent: retrieval-based eval targeting implicit criteria arxiv.org/abs/2504.15219

RoboInstruct: code generation for robotics with simulators arxiv.org/abs/2405.20179

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Reposted by Kanishka Misra 🌊

Kyle Mahowald (COLM 2025) @kmahowald.bsky.social · 2d

I’m at #COLM2025 from Wed with:

@siyuansong.bsky.social Tue am introspection arxiv.org/abs/2503.07513

@qyao.bsky.social Wed am controlled rearing: arxiv.org/abs/2503.20850

@sashaboguraev.bsky.social INTERPLAY ling interp: arxiv.org/abs/2505.16002

I’ll talk at INTERPLAY too. Come say hi!

Language Models Fail to Introspect About Their Knowledge of Language

There has been recent interest in whether large language models (LLMs) can introspect about their own internal states. Such abilities would make LLMs more interpretable, and also validate the use of s...

arxiv.org

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Reposted by Kanishka Misra 🌊

Siyuan Song✈️COLM @siyuansong.bsky.social · 2d

Heading to #COLM2025 to present my first paper w/ @jennhu.bsky.social @kmahowald.bsky.social !

When: Tuesday, 11 AM – 1 PM
Where: Poster #75

Happy to chat about my work and topics in computational linguistics & cogsci!

Also, I'm on the PhD application journey this cycle!

Paper info 👇:

Siyuan Song✈️COLM @siyuansong.bsky.social · Mar 12

New preprint w/ @jennhu.bsky.social @kmahowald.bsky.social : Can LLMs introspect about their knowledge of language?
Across models and domains, we did not find evidence that LLMs have privileged access to their own predictions. 🧵(1/8)

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Kanishka Misra 🌊 @kanishka.bsky.social · 2d

Also, I’m on the look out for my first PhD student! If you’d like to be the one, please reach out to me (dms/email open) and we can chat!!

@jessyjli.bsky.social and @kmahowald.bsky.social are also hiring students, and we’re all eager to co-advise!

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Kanishka Misra 🌊 @kanishka.bsky.social · 2d

I’ll also be moderating a roundtable at the INTERPLAY workshop on Oct 10 — excited to discuss behavior, representations, and a third secret thing with folks!

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Kanishka Misra 🌊 @kanishka.bsky.social · 2d

2. @sriramp05.bsky.social on LMs and Suspicious Coincidences at the PragLM workshop poster session on Friday, (work w/ me, @kmahowald.bsky.social, and @eunsol.bsky.social)

Paper: arxiv.org/abs/2504.09387

On Language Models' Sensitivity to Suspicious Coincidences

Humans are sensitive to suspicious coincidences when generalizing inductively over data, as they make assumptions as to how the data was sampled. This results in smaller, more specific hypotheses bein...

arxiv.org

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Kanishka Misra 🌊 @kanishka.bsky.social · 2d

Traveling to my first @colmweb.org🍁

Not presenting anything but here are two posters you should visit:

1. @qyao.bsky.social on Controlled rearing for direct and indirect evidence for datives (w/ me, @weissweiler.bsky.social and @kmahowald.bsky.social), W morning

Paper: arxiv.org/abs/2503.20850

Both Direct and Indirect Evidence Contribute to Dative Alternation Preferences in Language Models

Language models (LMs) tend to show human-like preferences on a number of syntactic phenomena, but the extent to which these are attributable to direct exposure to the phenomena or more general propert...

arxiv.org

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Reposted by Kanishka Misra 🌊

Jessy Li @jessyjli.bsky.social · 8d

All of us (@kanishka.bsky.social @kmahowald.bsky.social and me) are looking for PhD students this cycle! If computational linguistics/NLP is your passion, join us at UT Austin!

For my areas see jessyli.com

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Kanishka Misra 🌊 @kanishka.bsky.social · 8d

We'll all be attending #COLM2025 -- come say hi if you are interested in working with us!!

Separate tweet incoming for COLM papers!

Kanishka Misra 🌊 @kanishka.bsky.social · 8d

Understanding how internal representations drive conceptual behavior in LMs

E.g., arxiv.org/abs/2410.22590

Characterizing the Role of Similarity in the Property Inferences of Language Models

Property inheritance -- a phenomenon where novel properties are projected from higher level categories (e.g., birds) to lower level ones (e.g., sparrows) -- provides a unique window into how humans or...

arxiv.org

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Kanishka Misra 🌊 @kanishka.bsky.social · 8d

Role of language (vs. other modalities) in learning meaning-sensitivities

E.g., arxiv.org/abs/2507.13328

Vision-and-Language Training Helps Deploy Taxonomic Knowledge but Does Not Fundamentally Alter It

Does vision-and-language (VL) training change the linguistic representations of language models in meaningful ways? Most results in the literature have shown inconsistent or marginal differences, both...

arxiv.org

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Kanishka Misra 🌊 @kanishka.bsky.social · 8d

Using neural networks to generate experimental hypotheses about language acquisition in scenarios where hypothesis-spaces are intractable

E.g., arxiv.org/abs/2408.05086

Generating novel experimental hypotheses from language models: A case study on cross-dative generalization

Neural network language models (LMs) have been shown to successfully capture complex linguistic knowledge. However, their utility for understanding language acquisition is still debated. We contribute...

arxiv.org

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Kanishka Misra 🌊 @kanishka.bsky.social · 8d

"Controlled Rearing" of LMs to understand the role of input in acquiring linguistic generalization

E.g., arxiv.org/abs/2403.19827, arxiv.org/abs/2503.20850

Language Models Learn Rare Phenomena from Less Rare Phenomena: The Case of the Missing AANNs

Language models learn rare syntactic phenomena, but the extent to which this is attributable to generalization vs. memorization is a major open question. To that end, we iteratively trained transforme...

arxiv.org

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Kanishka Misra 🌊 @kanishka.bsky.social · 8d

The compling group at UT Austin (sites.utexas.edu/compling/) is looking for PhD students!

Come join me, @kmahowald.bsky.social, and @jessyjli.bsky.social as we tackle interesting research questions at the intersection of ling, cogsci, and ai!

Some topics I am particularly interested in:

Picture of the UT Tower with "UT Austin Computational Linguistics" written in bigger font, and "Humans processing computers processing human processing language" in smaller font

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Reposted by Kanishka Misra 🌊

Gemma Boleda @gboleda.bsky.social · 8d

New paper! 🚨 I argue that LLMs represent a synthesis between distributed and symbolic approaches to language, because, when exposed to language, they develop highly symbolic representations and processing mechanisms in addition to distributed ones.
arxiv.org/abs/2502.11856

Sigmoid function. Non-linearities in neural network allow it to behave in distributed and near-symbolic fashions.

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Kanishka Misra 🌊 @kanishka.bsky.social · 11d

Hehe but really — unifying all of them + easy access = yes plssss

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Kanishka Misra 🌊 @kanishka.bsky.social · 11d

Friendship ended with minicons, glazing is my new fav package!

Aaron Steven White @aaronstevenwhite.io · 11d

I've found it kind of a pain to work with resources like VerbNet, FrameNet, PropBank (frame files), and WordNet using existing tools. Maybe you have too. Here's a little package that handles data management, loading, and cross-referencing via either a CLI or a python API.

GitHub - aaronstevenwhite/glazing: Unified data models and interfaces for syntactic and semantic frame ontologies.

Unified data models and interfaces for syntactic and semantic frame ontologies. - aaronstevenwhite/glazing

github.com

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Kanishka Misra 🌊 @kanishka.bsky.social · 11d

God’s work 🙏

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Reposted by Kanishka Misra 🌊

Aaron Steven White @aaronstevenwhite.io · 11d

I've found it kind of a pain to work with resources like VerbNet, FrameNet, PropBank (frame files), and WordNet using existing tools. Maybe you have too. Here's a little package that handles data management, loading, and cross-referencing via either a CLI or a python API.

GitHub - aaronstevenwhite/glazing: Unified data models and interfaces for syntactic and semantic frame ontologies.

Unified data models and interfaces for syntactic and semantic frame ontologies. - aaronstevenwhite/glazing

github.com

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