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Jaap Jumelet

7 accepted papers

2025

Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models

EMNLP 2025

Seminal work by Huebner et al. (2021) showed that language models (LMs) trained on English Child-Directed Language (CDL) can outperform LMs trained on an equal amount of adult-directed text like Wikipedia. However, it remains unclear whether these results generalize across languages, architectures,

2025

TurBLiMP: A Turkish Benchmark of Linguistic Minimal Pairs

EMNLP 2025

We introduce TurBLiMP, the first Turkish benchmark of linguistic minimal pairs, designed to evaluate the linguistic abilities of monolingual and multilingual language models (LMs). Covering 16 linguistic phenomena with 1000 minimal pairs each, TurBLiMP fills an important gap in linguistic evaluation

2024

DecoderLens: Layerwise Interpretation of Encoder-Decoder Transformers

NAACL 2024findings

In recent years, several interpretability methods have been proposed to interpret the inner workings of Transformer models at different levels of precision and complexity.In this work, we propose a simple but effective technique to analyze encoder-decoder Transformers. Our method, which we name Deco…

2024

Do Language Models Exhibit Human-like Structural Priming Effects?

ACL 2024findings

We explore which linguistic factors—at the sentence and token level—play an important role in influencing language model predictions, and investigate whether these are reflective of results found in humans and human corpora (Gries and Kootstra, 2017). We make use of the structural priming paradigm—w…

2023

Transparency at the Source: Evaluating and Interpreting Language Models With Access to the True Distribution

EMNLP 2023long findings

We present a setup for training, evaluating and interpreting neural language models, that uses artificial, language-like data. The data is generated using a massive probabilistic grammar (based on state-split PCFGs), that is itself derived from a large natural language corpus, but also provides us c…

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