Do Language Models Exhibit Human-like Structural Priming Effects? (2024.findings-acl)
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| Challenge: | a recent exposure to a structure facilitates processing of the same structure, a study finds . structural priming is well attested in humans, for both language production and comprehension . |
| Approach: | They use the structural priming paradigm to investigate where priming effects manifest . they find that rarer elements within a prime increase priming effect . |
| Outcome: | The findings provide an important piece in the puzzle of understanding how properties within their context affect structural prediction in language models. |
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Cognitive Effects and Biases in Large Language Models (2026.eacl-tutorials)
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| Challenge: | This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models. |
| Approach: | This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models. |
| Outcome: | This tutorial bridges psychology and NLP to clarify cognitive effects and biases in large language models. |
Analysis of the Neglect-Zero Effect in Large Language Models (2026.acl-srw)
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| Challenge: | a neglect-zero effect is a human cognitive bias in language processing . it is unclear whether LLMs also exhibit this effect . |
| Approach: | They focus on a human cognitive bias called the *neglect-zero effect* . they propose a paradigm where exposure to a preceding sentence facilitates processing of a subsequent sentence due to their similarity. |
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How Do Language Models Acquire Character-Level Information? (2026.eacl-long)
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| Challenge: | Language models (LMs) implicitly encode character-level information, despite not being explicitly provided during training. |
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Predicting Reference: What do Language Models Learn about Discourse Models? (2020.emnlp-main)
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| Challenge: | a growing literature that probes neural language models to assess their latent acquisition of grammatical knowledge has not investigated their acquisition of discourse modeling ability. |
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Injecting structural hints: Using language models to study inductive biases in language learning (2023.findings-emnlp)
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| Challenge: | a recent study examines the cognitive inductive biases that make language learning possible. |
| Approach: | They structurally bias transformer language models by pretraining on synthetic data . they then evaluate their inductive biases by fine-tuning on three different languages . |
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Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers) (N18-3)
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| Challenge: | NAACL 2018 Industry Track aims to provide a forum for researchers, engineers and application developers to share their experience in real-world language problems. |
| Approach: | NAACL 2018 Industry Track is the inaugural conference in the *ACL family of conferences . organizers wanted to provide a forum for researchers, engineers and application developers to share their experience . six of the papers were desk rejects due to non-conformance with submission requirements . |
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Some of Them Can be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers (P18-2)
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| Challenge: | cloze deletion test is a test that requires the learner to understand the context and vocabulary in order to identify the correct word. |
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Language Models Learn Universal Representations of Numbers and Here’s Why You Should Care (2026.acl-long)
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Michal Štefánik, Timothee Mickus, Marek Kadlčík, Bertram Højer, Michal Spiegel, Raúl Vázquez, Aman Sinha, Josef Kuchař, Philipp Mondorf, Pontus Stenetorp
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Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers) (N19-2)
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| Challenge: | NAACL-HLT 2018 introduced the industry track in 2018 . the track provides a forum for researchers, engineers and application developers to exchange ideas . |
| Approach: | NAACL-HLT 2018 introduced the industry track at the conference in new orleans . the track provides a forum for researchers, engineers and application developers to exchange ideas . |
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Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (D18-2)
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| Challenge: | 77 submissions were received for the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) 4 of the 73 valid submissions received were either invalid or withdrawn by the authors. |
| Approach: | The volume contains papers from the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) 4 of the 77 submissions were either invalid or withdrawn by the authors. |
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