| Challenge: | Pretrained language models generate grammatically well-formed text and discriminate well between grammatical and ungrammatically sentences in tightly controlled minimal pairs. |
| Approach: | They propose a method to train pretrained LMs for representations of grammaticality by applying perturbations to a naturalistic text corpus. |
| Outcome: | The proposed model outperforms probability-based models on human-curated grammaticality judgment benchmarks and performs worse than string probabilities on plausibility benchmarks. |
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What Can String Probability Tell Us About Grammaticality? (2026.tacl-1)
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| Challenge: | linguistic theories have argued that language models have largely achieved grammatical competence, but they will assign non-zero probability to all strings. |
| Approach: | They propose a theoretical framework for analyzing string probabilities in linguistics based on simple assumptions about the generative process of corpus data. |
| Outcome: | The proposed framework makes three predictions using 280K sentence pairs in English and Chinese. |
Can Language Models Learn Typologically Implausible Languages? (2026.tacl-1)
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| Challenge: | Language models provide a naturalistic framework for studying artificial language learning . authors: typological universals and tendencies are thought to be caused by a learning bias . |
| Approach: | They propose to train LMs on highly naturalistic counterfactual versions of English and Japanese . they show that LM learn subtly implausible languages more slowly . |
| Outcome: | The proposed language models learn subtly implausible languages more slowly compared to human models . the findings suggest that LMs exhibit typologically aligned learning preferences . |
Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models (2023.emnlp-main)
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| Challenge: | Abstract grammatical knowledge is key to linguistic generalization in humans . strong evidence for grammatikal abstraction in humans comes from structural priming . |
| Approach: | They compare human models of crosslingual structural priming to human models . they find evidence for abstract monolingual and crosslingual grammatical representations . |
| Outcome: | The results show that grammatical representations in multilingual models are similar to humans . the strongest evidence for grammatikal abstraction in humans comes from structural priming . |
RNN Simulations of Grammaticality Judgments on Long-distance Dependencies (C18-1)
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| Challenge: | LSTM networks can detect linguistic structures which are ungrammatical due to extraction violations, but are sensitive to linguistic processing factors. |
| Approach: | They propose to use LSTM networks to detect ungrammatical sentences by detecting extra arguments and subject-relative clause island violations. |
| Outcome: | The proposed model can correctly classify (un)grammatical sentences, in certain conditions, but is sensitive to linguistic processing factors and unable to induce a more abstract notion of grammaticality. |
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. |
Large Language Models for Psycholinguistic Plausibility Pretesting (2024.findings-eacl)
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| Challenge: | Psycholinguists typically use language models to create controlled materials . plausibility judgments are often based on coarse-grained judgements, but fine-grounded ones do not . |
| Approach: | They investigate whether Language Models can be used to generate plausibility judgments . they find that plausible judgements from LMs are highly related to human judgements - whereas other LM models are not . |
| Outcome: | The proposed language models can generate plausibility judgments from human evaluators . the proposed models do not provide satisfactory discriminative power . |
How to Make the Most of LLMs’ Grammatical Knowledge for Acceptability Judgments (2025.naacl-long)
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Yusuke Ide, Yuto Nishida, Justin Vasselli, Miyu Oba, Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
| Challenge: | Conventional approaches compare sentence probabilities directly, but large language models (LLMs) provide nuanced evaluation methods using prompts and templates. |
| Approach: | They propose to derive acceptability judgments from large language models using prompts and templates to comprehensively evaluate their grammatical knowledge. |
| Outcome: | The proposed methods excel in different linguistic phenomena, suggesting they access different aspects of the LLMs’ grammatical knowledge. |
Probing Pretrained Language Models for Lexical Semantics (2020.emnlp-main)
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| Challenge: | Existing studies have focused on morphosyntactic, semantic, and world knowledge, but it remains unclear to what extent LMs derive lexical type-level knowledge from words in context. |
| Approach: | They propose to use multilingual and monolingual LMs to extract lexical type-level knowledge from words in context. |
| Outcome: | The proposed models perform well across six typologically diverse languages and five lexical tasks. |
Do Language Models Perform Generalizable Commonsense Inference? (2021.findings-acl)
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| Challenge: | Recent work has applied pretrained language models to populate commonsense knowledge graphs (CKGs) but there is a lack of understanding on their generalization to multiple CKGs, unseen relations, and novel entities. |
| Approach: | They analyze the ability of pretrained language models to perform generalizable commonsense inference in terms of knowledge capacity, transferability and induction. |
| Outcome: | The proposed models can adapt to different schemas defined by multiple CKGs but fail to generalize to new relations. |
The Grammar-Learning Trajectories of Neural Language Models (2022.acl-long)
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| Challenge: | In this paper, we show that neural language models with different initialization, architecture, and training data acquire linguistic phenomena in a similar order, despite their different end performance. |
| Approach: | They propose to use mutual inductive bias to study linguistic representations implicit in NLMs. |
| Outcome: | The proposed approach shows that NLMs with different initialization, architecture, and training data acquire linguistic phenomena in a similar order, despite their different end performance. |