Memory efficiency and resource-rational encoding in sentence processing (2026.acl-long)
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| Challenge: | Existing studies have shown that language models need to be constrained in their use of working memory for context, the analogue to human working memory (WM). |
| Approach: | They propose to inject noise into hidden representations of Transformer-based LMs to capture constraint on memory encoding. |
| Outcome: | The proposed model improves alignment with human reading times and makes them more compressed and categorical. |
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| Challenge: | Existing methods do not directly target the balance between memory and sentence processing, which is central to human working memory. |
| Approach: | They propose a dual-task paradigm that combines arithmetic computation with sentence comprehension . they show a greater accuracy gap between plausible sentences and implausible sentences . |
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Working Memory Constraints Scaffold Learning in Transformers under Data Scarcity (2026.findings-acl)
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| Challenge: | a recent study has shown that human-like working memory constraints can be integrated into the Transformer architecture . our model incorporates fixed-width windows and temporal decay based attention mechanisms . |
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| Challenge: | a fundamental question in psycholinguistics is how comprehenders form interpretations of utterances that they hear or see. |
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Context Limitations Make Neural Language Models More Human-Like (2022.emnlp-main)
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| Challenge: | Language models (LMs) have been used in cognitive modeling and engineering studies to simulate human cognitive load during reading. |
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| Challenge: | a recent study shows that large language models are capable of inducing rich representations of data that are seen in-context . a novel task, adaptive world modeling, shows that even the most performant LLMs cannot reliably leverage novel semantics defined in-constitut. |
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Working Memory Identifies Reasoning Limits in Language Models (2024.emnlp-main)
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| Challenge: | Using large language models, we examine the limitations of their cognitive capabilities and their working memory. |
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Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide (2026.tacl-1)
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| Challenge: | Pre-trained language models provide strong foundations, but effective adaptation under data scarcity requires efficient and efficient fine-tuning techniques. |
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Long-Range Language Modeling with Selective Cache (2023.findings-emnlp)
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| Challenge: | Existing models that use transformers to model language cost quadratically increase with sequence length. |
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Large Language Models with Controllable Working Memory (2023.findings-acl)
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Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix Yu, Sanjiv Kumar
| Challenge: | Large language models (LLMs) have led to a series of breakthroughs in natural language processing due to the massive amounts of world knowledge they memorize during pretraining. |
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Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing (2024.naacl-long)
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| Challenge: | Large language models have demonstrated considerable success in various natural language processing tasks, but their performance in NMT tasks is still underexplored. |
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