Papers by Evelina Fedorenko
SentSpace: Large-Scale Benchmarking and Evaluation of Text using Cognitively Motivated Lexical, Syntactic, and Semantic Features (2022.naacl-demo)
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| Challenge: | SentSpace provides a framework for streamlined evaluation of textual input. |
| Approach: | They describe the design of SentSpace and demonstrate an example use case . they use a web interface for interactive visualization and comparison with large corpora . |
| Outcome: | The framework provides a common framework for evaluation and visualization. |
The time scale of redundancy between prosody and linguistic context (2025.acl-long)
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Tamar I Regev, Chiebuka Ohams, Shaylee Xie, Lukas Wolf, Evelina Fedorenko, Alex Warstadt, Ethan Wilcox, Tiago Pimentel
| Challenge: | Prior work has shown that the information carried by prosodic features is substantially redundant with that carried by the surrounding words. |
| Approach: | They examine the time scale of this relationship, studying how it varies with the length of past and future contexts. |
| Outcome: | The results show that prosody features show some redundancy with future words, but only with a short scale of 1-2 words, consistent with reports of incremental short-term planning in language production. |
Different types of syntactic agreement recruit the same units within large language models (2026.acl-long)
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| Challenge: | Large language models can reliably distinguish grammatical from ungrammatically sentences, but how gramatical knowledge is represented within the models remains an open question. |
| Approach: | They use a functional localization approach inspired by cognitive neuroscience to identify the LLM units most responsive to 67 English syntactic phenomena in seven open-weight models. |
| Outcome: | The proposed model is most responsive to 67 English syntactic phenomena and consistently supports model performance. |
Quantifying the redundancy between prosody and text (2023.emnlp-main)
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Lukas Wolf, Tiago Pimentel, Evelina Fedorenko, Ryan Cotterell, Alex Warstadt, Ethan Wilcox, Tamar Regev
| Challenge: | Existing studies suggest partial redundancy between prosody and linguistic information. |
| Approach: | They use large language models to estimate how much information is redundant between prosody and the words themselves. |
| Outcome: | The proposed model can predict prosodic features across prosodic features, including intensity, duration, pauses, and pitch contours. |
The Natural Stories Corpus (L18-1)
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Richard Futrell, Edward Gibson, Harry J. Tily, Idan Blank, Anastasia Vishnevetsky, Steven Piantadosi, Evelina Fedorenko
| Challenge: | Existing corpora of naturalistic text do not contain the low-frequency syntactic constructions needed to distinguish between theories. |
| Approach: | They propose to compare models of language processing by comparing their ability to predict behavioral and neural measures of processing difficulty to corpora of naturalistic text. |
| Outcome: | The proposed corpus contains low-frequency syntactic constructions while sounding fluent to native speakers. |
A fine-grained comparison of pragmatic language understanding in humans and language models (2023.acl-long)
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| Challenge: | Pragmatics and non-literal language understanding are essential to human communication . a long-standing challenge for artificial language models is to capture pragmatics . |
| Approach: | They compare language models and humans on seven pragmatic phenomena using curated English materials. |
| Outcome: | The proposed model achieves high accuracy and matches human error patterns . the results suggest pragmatic behaviors can emerge in models without explicit representations of mental states . |
Visual Grounding Helps Learn Word Meanings in Low-Data Regimes (2024.naacl-long)
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| Challenge: | Modern neural language models (LMs) require distinctly un-human-like ways to achieve these results. |
| Approach: | They train a diverse set of LM architectures with and without auxiliary visual supervision on datasets of varying scales. |
| Outcome: | The proposed models exhibit better learning of syntactic categories, lexical relations, semantic features, word similarity and alignment with human neural representations. |
Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling (2024.findings-acl)
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| Challenge: | Neural language models (LMs) are trained on orders of magnitude more language data than human language learners receive, but without supervision from other sensory modalities that play a crucial role in human learning. |
| Approach: | They propose a grounded language learning procedure that leverages visual supervision to improve textual representations. |
| Outcome: | The proposed procedure outperforms standard language-only models in terms of learning efficiency in small and developmentally plausible data regimes and improves perplexity by around 5% on multiple language modeling tasks compared to other models trained on the same amount of text data. |