| 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. |
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Refining Targeted Syntactic Evaluation of Language Models (2021.naacl-main)
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| Challenge: | Targeted syntactic evaluation of subject-verb number agreement in English (TSE) evaluates language models’ syntaktic knowledge using hand-crafted minimal pairs of sentences that differ only in the main verb’s conjugation. |
| Approach: | They propose to use templates to evaluate language models' syntactic knowledge to assess their ability to conjugate arbitrary verbs and their likely behavior to measure their likelihood of conjugating grammatical sentences. |
| Outcome: | The proposed evaluations overestimate systematicity of language models, but score up to 40% better on verbs that they predict are likely in context. |
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. |
| Outcome: | The proposed method reproduces positive results with two non-syntactic baseline language models: an n-gram model and an LSTM model trained on scrambled inputs. |
Cross-Linguistic Syntactic Evaluation of Word Prediction Models (2020.acl-main)
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| 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. |
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. |
| Outcome: | The proposed models are trained on large datasets and only sensitive to subtle cues . the results raise questions about the accuracy of the models and their performance . |
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. |
| Approach: | They propose to make targeted syntactic evaluations accessible to both experts in NLP and linguistics and reproducible across computing environments. |
| Outcome: | The proposed framework is reproducible across computing environments and standardized following the norms of psycholinguistic experimental design. |
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 . |
| Outcome: | The proposed framework is based on translated text and language models on 21 languages. |
Targeted Syntactic Evaluation on the Chomsky Hierarchy (2024.lrec-main)
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| Challenge: | a novel evaluation paradigm for targeted syntactic evaluations is proposed . we create formal languages that abstract four syntaktic phenomena in natural languages . |
| Approach: | They propose a new evaluation paradigm for Targeted Syntactic Evaluations . they create formal languages that abstract syntactical phenomena in natural languages . |
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Language Models at the Syntax-Semantics Interface: A Case Study of the Long-Distance Binding of Chinese Reflexive Ziji (2025.coling-main)
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| Challenge: | Existing language models tend to rely heavily on sequential cues, but not always favoring the closest strings. |
| Approach: | They construct a dataset of 320 synthetic sentences and 360 natural sentences from the BCC corpus . they evaluate 21 language models against this dataset and compare their performance to native Mandarin speakers . |
| Outcome: | The proposed models do not replicate human-like judgments in Mandarin Chinese . the results show that existing models tend to rely heavily on sequential cues . |
Discovering Language Model Behaviors with Model-Written Evaluations (2023.findings-acl)
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Ethan Perez, Sam Ringer, Kamile Lukosiute, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, Andy Jones, Anna Chen, Benjamin Mann, Brian Israel, Bryan Seethor, Cameron McKinnon, Christopher Olah, Da Yan, Daniela Amodei, Dario Amodei, Dawn Drain, Dustin Li, Eli Tran-Johnson, Guro Khundadze, Jackson Kernion, James Landis, Jamie Kerr, Jared Mueller, Jeeyoon Hyun, Joshua Landau, Kamal Ndousse, Landon Goldberg, Liane Lovitt, Martin Lucas, Michael Sellitto, Miranda Zhang, Neerav Kingsland, Nelson Elhage, Nicholas Joseph, Noemi Mercado, Nova DasSarma, Oliver Rausch, Robin Larson, Sam McCandlish, Scott Johnston, Shauna Kravec, Sheer El Showk, Tamera Lanham, Timothy Telleen-Lawton, Tom Brown, Tom Henighan, Tristan Hume, Yuntao Bai, Zac Hatfield-Dodds, Jack Clark, Samuel R. Bowman, Amanda Askell, Roger Grosse, Danny Hernandez, Deep Ganguli, Evan Hubinger, Nicholas Schiefer, Jared Kaplan
| Challenge: | Prior work creates evaluations with crowdwork or existing data sources, which are not always available. |
| Approach: | They generate evaluations automatically with language models (LMs) using crowdwork or existing data sources to find out how they behave . |
| Outcome: | The results show that large LMs repeat back a dialog user’s preferred answer and express greater desire to pursue concerning goals like resource acquisition and goal preservation. |
Evaluating Large Language Models via Linguistic Profiling (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) undergo extensive evaluation against various benchmarks collected in established leaderboards to assess their performance across multiple tasks. |
| Approach: | They propose a new evaluation methodology to test LLMs' sentence generation abilities under specific linguistic constraints. |
| Outcome: | The proposed evaluation methodology is based on the 'linguistic profiling' approach and is not intended to be a task-oriented evaluation. |