Imagen de Portada

Manipulating language models' training data to study syntactic constraint learning: the case of English passivization

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Leong, Cara Su-Yi, Linzen, Tal
Formato:	Preprint
Publicado:	2024
Materias:	Computation and Language
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Why are language models less surprised than humans? Testing the Parse Multiplicity Mismatch Hypothesis
por: Timkey, William, et al.
Publicado: (2026)

SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser
por: Prasad, Grusha, et al.
Publicado: (2024)

Do Language Models' Words Refer?
por: Mandelkern, Matthew, et al.
Publicado: (2023)

Deconstructing sentence disambiguation by joint latent modeling of reading paradigms: LLM surprisal is not enough
por: Paape, Dario, et al.
Publicado: (2026)

Disambiguation of morpho-syntactic features of African American English -- the case of habitual be
por: Santiago, Harrison, et al.
Publicado: (2022)

Entailment Semantics Can Be Extracted from an Ideal Language Model
por: Merrill, William, et al.
Publicado: (2022)

Evaluating In-Context Translation with Synchronous Context-Free Grammar Transduction
por: Petty, Jackson, et al.
Publicado: (2026)

Generating event descriptions under syntactic and semantic constraints
por: Cao, Angela, et al.
Publicado: (2024)

LLMs syntactically adapt their language use to their conversational partner
por: Kandra, Florian, et al.
Publicado: (2025)

How language models extrapolate outside the training data: A case study in Textualized Gridworld
por: Kim, Doyoung, et al.
Publicado: (2024)

What Goes Into a LM Acceptability Judgment? Rethinking the Impact of Frequency and Length
por: Tjuatja, Lindia, et al.
Publicado: (2024)

In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax
por: Mueller, Aaron, et al.
Publicado: (2023)

How Does Code Pretraining Affect Language Model Task Performance?
por: Petty, Jackson, et al.
Publicado: (2024)

Multilingual Prompting for Improving LLM Generation Diversity
por: Wang, Qihan, et al.
Publicado: (2025)

Different types of syntactic agreement recruit the same units within large language models
por: Kryvosheieva, Daria, et al.
Publicado: (2025)

Can You Learn Semantics Through Next-Word Prediction? The Case of Entailment
por: Merrill, William, et al.
Publicado: (2024)

Emergence of Linear Truth Encodings in Language Models
por: Ravfogel, Shauli, et al.
Publicado: (2025)

A hierarchical Bayesian model for syntactic priming
por: Xu, Weijie, et al.
Publicado: (2024)

Language Models Struggle to Use Representations Learned In-Context
por: Lepori, Michael A., et al.
Publicado: (2026)

Formalising lexical and syntactic diversity for data sampling in French
por: Estève, Louis, et al.
Publicado: (2025)

Counting trees: A treebank-driven exploration of syntactic variation in speech and writing across languages
por: Dobrovoljc, Kaja
Publicado: (2025)

Tiny language models
por: Gross, Ronit D., et al.
Publicado: (2025)

Rapid Word Learning Through Meta In-Context Learning
por: Wang, Wentao, et al.
Publicado: (2025)

The distribution of syntactic dependency distances
por: Petrini, Sonia, et al.
Publicado: (2022)

Automata-based constraints for language model decoding
por: Koo, Terry, et al.
Publicado: (2024)

Exploring language relations through syntactic distances and geographic proximity
por: De Gregorio, Juan, et al.
Publicado: (2024)

Dynamic data sampler for cross-language transfer learning in large language models
por: Li, Yudong, et al.
Publicado: (2024)

RELIC: Evaluating Complex Reasoning via the Recognition of Languages In-Context
por: Petty, Jackson, et al.
Publicado: (2025)

The Impact of Depth on Compositional Generalization in Transformer Language Models
por: Petty, Jackson, et al.
Publicado: (2023)

Can language models learn analogical reasoning? Investigating training objectives and comparisons to human performance
por: Petersen, Molly R., et al.
Publicado: (2023)

Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic Biases
por: Hu, Michael Y., et al.
Publicado: (2025)

Always Learning, Always Mixing: Efficient and Simple Data Mixing All The Time
por: Hu, Michael Y., et al.
Publicado: (2026)

Large language models have learned to use language
por: Lupyan, Gary
Publicado: (2025)

Bayesian Teaching Enables Probabilistic Reasoning in Large Language Models
por: Qiu, Linlu, et al.
Publicado: (2025)

We can still parse using syntactic rules
por: Hussein, Ghaly
Publicado: (2026)

Symbol tuning improves in-context learning in language models
por: Wei, Jerry, et al.
Publicado: (2023)

Data Augmentation With Back translation for Low Resource languages: A case of English and Luganda
por: Kimera, Richard, et al.
Publicado: (2025)

Revisiting subword tokenization: A case study on affixal negation in large language models
por: Truong, Thinh Hung, et al.
Publicado: (2024)

Do we really have to filter out random noise in pre-training data for language models?
por: Ru, Jinghan, et al.
Publicado: (2025)

StatLLaMA: Multi-Stage training for domain-optimized statistical large language models
por: Zeng, Jing-Yi, et al.
Publicado: (2025)