| Challenge: | A range of studies have concluded that neural word prediction models can distinguish grammatical from ungrammatically sentences with high accuracy. |
| Approach: | They propose to use CLAMS to evaluate LSTM and multilingual BERT models. |
| Outcome: | The proposed model can learn syntax on English, French, German, Hebrew and Russian, and LSTM language models on multilingual and multilingual models. |
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Targeted Syntactic Evaluation of Language Models (D18-1)
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| Challenge: | Recent advances have led to an explosion of neural network-based LM architectures. |
| Approach: | They propose to supplement perplexity with a metric that assesses whether a language model can predict the grammatical sentence more accurately than an ungrammatically-based model. |
| Outcome: | The proposed model performed poorly on many of the constructions. |
Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)
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| Challenge: | Several testing methodologies have been developed to probe models’ syntactic representations. |
| Approach: | They propose a method to determine syntactic structure by training a model on strings generated according to a template and testing its ability to distinguish between similar ones with different syntax. |
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Are All Languages Equally Hard to Language-Model? (N18-2)
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| Challenge: | a fair comparison of language models is tricky because of the size of the corpora and the variability of orthographic systems. |
| Approach: | They propose a framework for fair cross-linguistic comparison of language models . they show that in some languages, textual expression is harder to predict with n-gram models compared to LSTM models based on translated text . |
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How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions (P19-1)
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| Challenge: | Cross-lingual word embeddings (CLEs) are used for downstream NLP tasks . CLEs are based on bilingual lexicon induction (BLI) evaluations vary greatly, hindering ability to interpret performance and properties of different CLE models. |
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Assessing the Syntactic Capabilities of Transformer-based Multilingual Language Models (2021.findings-acl)
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| Challenge: | Multilingual Transformer-based language models have been shown to be excellent learners in crosslingual transfer tasks. |
| Approach: | They evaluate the syntactic generalization capabilities of BERT and RoBERTa models on English and Spanish tests. |
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7 Points to Tsinghua but 10 Points to ? Assessing Large Language Models in Agentic Multilingual National Bias (2025.findings-acl)
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| Challenge: | Large Language Models have garnered significant attention for their capabilities in multilingual natural language processing, but studies on risks associated with cross biases are limited to immediate context preferences. |
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SyntaxGym: An Online Platform for Targeted Evaluation of Language Models (2020.acl-demos)
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| Challenge: | SyntaxGym is an online platform and open-source framework for targeted syntactic evaluation of neural network language models. |
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Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs (2025.acl-long)
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Sumanth Doddapaneni, Mohammed Safi Ur Rahman Khan, Dilip Venkatesh, Raj Dabre, Anoop Kunchukuttan, Mitesh M Khapra
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A Systematic Assessment of Syntactic Generalization in Neural Language Models (2020.acl-main)
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| Challenge: | Existing work on syntactic knowledge models has not provided a clear picture of the properties required to produce proper syntaktic generalizations. |
| Approach: | They propose to evaluate syntactic knowledge of language models by varying model architectures . they find substantial differences in syntaktic generalization performance by model architecture . |
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Neural language models as psycholinguistic subjects: Representations of syntactic state (N19-1)
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| Challenge: | a recent study examines the extent to which neural network language models reflect incremental representations of syntactic state . we examine neural network model behavior on sentences chosen to probe specific aspects of the learned representations . |
| Approach: | They employ experimental methodologies developed in psycholinguistics to study syntactic representation in the human mind. |
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