Search Results for author: Kyle Mahowald

Found 33 papers, 15 papers with code

Investigating Information-Theoretic Properties of the Typology of Spatial Demonstratives

no code implementations NAACL (SIGTYP) 2022 Sihan Chen, Richard Futrell, Kyle Mahowald

Using data from Nintemann et al. (2020), we explore the variability in complexity and informativity across spatial demonstrative systems using spatial deictic lexicons from 223 languages.

When classifying grammatical role, BERT doesn’t care about word order... except when it matters

no code implementations ACL 2022 Isabel Papadimitriou, Richard Futrell, Kyle Mahowald

Because meaning can often be inferred from lexical semantics alone, word order is often a redundant cue in natural language.

Participle-Prepended Nominals Have Lower Entropy Than Nominals Appended After the Participle

no code implementations16 May 2024 Kristie Denlinger, Stephen Wechsler, Kyle Mahowald

That is, we compare the entropy of $\alpha$ in compound construction slots like $\alpha$-[V]ed to the entropy of $\alpha$ in phrasal constructions like [V]ed by $\alpha$ for a given verb V. As predicted, there is significantly lower entropy in the compound construction than in the phrasal construction.

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

no code implementations28 Mar 2024 Kanishka Misra, Kyle Mahowald

Training on a corpus of human-scale in size (100M words), we iteratively trained transformer language models on systematically manipulated corpora and then evaluated their learning of a particular rare grammatical phenomenon: the English Article+Adjective+Numeral+Noun (AANN) construction (``a beautiful five days'').

counterfactual Memorization

Mission: Impossible Language Models

1 code implementation12 Jan 2024 Julie Kallini, Isabel Papadimitriou, Richard Futrell, Kyle Mahowald, Christopher Potts

Chomsky and others have very directly claimed that large language models (LLMs) are equally capable of learning languages that are possible and impossible for humans to learn.

Experimental Contexts Can Facilitate Robust Semantic Property Inference in Language Models, but Inconsistently

no code implementations12 Jan 2024 Kanishka Misra, Allyson Ettinger, Kyle Mahowald

Recent zero-shot evaluations have highlighted important limitations in the abilities of language models (LMs) to perform meaning extraction.

Novel Concepts

Revisiting the Optimality of Word Lengths

no code implementations6 Dec 2023 Tiago Pimentel, Clara Meister, Ethan Gotlieb Wilcox, Kyle Mahowald, Ryan Cotterell

Under this method, we find that a language's word lengths should instead be proportional to the surprisal's expectation plus its variance-to-mean ratio.

Counterfactually Probing Language Identity in Multilingual Models

1 code implementation29 Oct 2023 Anirudh Srinivasan, Venkata S Govindarajan, Kyle Mahowald

We use one such technique, AlterRep, a method of counterfactual probing, to explore the internal structure of multilingual models (mBERT and XLM-R).

counterfactual Masked Language Modeling +1

Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike Ways

1 code implementation26 Oct 2023 Venkata S Govindarajan, Juan Diego Rodriguez, Kaj Bostrom, Kyle Mahowald

We pretrained our masked language models with three ingredients: an initial pretraining with music data, training on shorter sequences before training on longer ones, and masking specific tokens to target some of the BLiMP subtasks.

Language Modelling Masked Language Modeling

Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias

1 code implementation25 May 2023 Venkata S Govindarajan, Kyle Mahowald, David I. Beaver, Junyi Jessy Li

While existing work on studying bias in NLP focues on negative or pejorative language use, Govindarajan et al. (2023) offer a revised framing of bias in terms of intergroup social context, and its effects on language behavior.

counterfactual Specificity

Elaborative Simplification as Implicit Questions Under Discussion

no code implementations17 May 2023 Yating Wu, William Sheffield, Kyle Mahowald, Junyi Jessy Li

Automated text simplification, a technique useful for making text more accessible to people such as children and emergent bilinguals, is often thought of as a monolingual translation task from complex sentences to simplified sentences using encoder-decoder models.

Decoder Question Generation +2

For Generated Text, Is NLI-Neutral Text the Best Text?

1 code implementation16 Feb 2023 Michail Mersinias, Kyle Mahowald

We explore incorporating natural language inference (NLI) into the text generative pipeline by using a pre-trained NLI model to assess whether a generated sentence entails, contradicts, or is neutral to the prompt and preceding text.

Natural Language Inference Sentence +1

A Discerning Several Thousand Judgments: GPT-3 Rates the Article + Adjective + Numeral + Noun Construction

no code implementations29 Jan 2023 Kyle Mahowald

I validate the prompt using the CoLA corpus of acceptability judgments and then zero in on the AANN construction.


Dissociating language and thought in large language models

no code implementations16 Jan 2023 Kyle Mahowald, Anna A. Ivanova, Idan A. Blank, Nancy Kanwisher, Joshua B. Tenenbaum, Evelina Fedorenko

Large Language Models (LLMs) have come closest among all models to date to mastering human language, yet opinions about their linguistic and cognitive capabilities remain split.

Inducing Character-level Structure in Subword-based Language Models with Type-level Interchange Intervention Training

1 code implementation19 Dec 2022 Jing Huang, Zhengxuan Wu, Kyle Mahowald, Christopher Potts

Language tasks involving character-level manipulations (e. g., spelling corrections, arithmetic operations, word games) are challenging for models operating on subword units.

Spelling Correction

Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality

1 code implementation1 Nov 2022 Anuj Diwan, Layne Berry, Eunsol Choi, David Harwath, Kyle Mahowald

Recent visuolinguistic pre-trained models show promising progress on various end tasks such as image retrieval and video captioning.

Data Augmentation Image Retrieval +2

What do tokens know about their characters and how do they know it?

1 code implementation NAACL 2022 Ayush Kaushal, Kyle Mahowald

Pre-trained language models (PLMs) that use subword tokenization schemes can succeed at a variety of language tasks that require character-level information, despite lacking explicit access to the character composition of tokens.

When classifying grammatical role, BERT doesn't care about word order... except when it matters

1 code implementation11 Mar 2022 Isabel Papadimitriou, Richard Futrell, Kyle Mahowald

Because meaning can often be inferred from lexical semantics alone, word order is often a redundant cue in natural language.

Grammatical cues to subjecthood are redundant in a majority of simple clauses across languages

no code implementations30 Jan 2022 Kyle Mahowald, Evgeniia Diachek, Edward Gibson, Evelina Fedorenko, Richard Futrell

The conclusion is that grammatical cues such as word order are necessary to convey subjecthood and objecthood in a minority of naturally occurring transitive clauses; nevertheless, they can (a) provide an important source of redundancy and (b) are crucial for conveying intended meaning that cannot be inferred from the words alone, including descriptions of human interactions, where roles are often reversible (e. g., Ray helped Lu/Lu helped Ray), and expressing non-prototypical meanings (e. g., "The bone chewed the dog.

Sentence World Knowledge

A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space

1 code implementation EMNLP 2021 Alex Jones, William Yang Wang, Kyle Mahowald

We verify some of our linguistic findings by looking at the effect of morphological segmentation on English-Inuktitut alignment, in addition to examining the effect of word order agreement on isomorphism for 66 zero-shot language pairs from a different corpus.

Retrieval Sentence

How (Non-)Optimal is the Lexicon?

no code implementations NAACL 2021 Tiago Pimentel, Irene Nikkarinen, Kyle Mahowald, Ryan Cotterell, Damián Blasi

Examining corpora from 7 typologically diverse languages, we use those upper bounds to quantify the lexicon's optimality and to explore the relative costs of major constraints on natural codes.

Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP

1 code implementation NeurIPS 2021 Joshua Rozner, Christopher Potts, Kyle Mahowald

Cryptic crosswords, the dominant crossword variety in the UK, are a promising target for advancing NLP systems that seek to process semantically complex, highly compositional language.

Language Modelling

Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERT

1 code implementation EACL 2021 Isabel Papadimitriou, Ethan A. Chi, Richard Futrell, Kyle Mahowald

Further examining the characteristics that our classifiers rely on, we find that features such as passive voice, animacy and case strongly correlate with classification decisions, suggesting that mBERT does not encode subjecthood purely syntactically, but that subjecthood embedding is continuous and dependent on semantic and discourse factors, as is proposed in much of the functional linguistics literature.


With Little Power Comes Great Responsibility

2 code implementations EMNLP 2020 Dallas Card, Peter Henderson, Urvashi Khandelwal, Robin Jia, Kyle Mahowald, Dan Jurafsky

Despite its importance to experimental design, statistical power (the probability that, given a real effect, an experiment will reject the null hypothesis) has largely been ignored by the NLP community.

Experimental Design Machine Translation +1

Response to Liu, Xu, and Liang (2015) and Ferrer-i-Cancho and Gómez-Rodríguez (2015) on Dependency Length Minimization

no code implementations1 Oct 2015 Richard Futrell, Kyle Mahowald, Edward Gibson

We address recent criticisms (Liu et al., 2015; Ferrer-i-Cancho and G\'omez-Rodr\'iguez, 2015) of our work on empirical evidence of dependency length minimization across languages (Futrell et al., 2015).

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