| Challenge: | Several studies suggest that readers do adapt their lexical and syntactic predictions to the current context. |
| Approach: | They propose to add a simple adaptation mechanism to a neural language model to improve predictions of reading times. |
| Outcome: | The proposed model improves predictions of human reading times compared to a non-adaptive model. |
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Scaling in Cognitive Modelling: a Multilingual Approach to Human Reading Times (2023.acl-short)
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| Challenge: | Neural language models provide conditional probability distributions over the lexicon that are predictive of human processing times. |
| Approach: | They propose to use a transformer-based model to generate probabilistic estimates that are less predictive of early eye-tracking measurements reflecting lexical access and early semantic integration. |
| Outcome: | The proposed models show that larger models capture late eye-tracking measurements that reflect the full integration of a word into the current language context. |
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. |
| Approach: | They propose to constrain LMs' context access to improve their simulation of human reading behavior by incorporating syntactic biases into their context access. |
| Outcome: | The proposed model improves the simulation of human reading behavior by incorporating syntactic biases into their context access. |
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 . |
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. |
| Approach: | They draw on a psycholinguistic literature that has established how different contexts affect referential biases concerning who is likely to be referred to next. |
| Outcome: | The proposed models do not resemble human language users, the authors show . their models capture the linguistic knowledge required to perform discourse modeling . |
The Trade-offs of Domain Adaptation for Neural Language Models (2022.acl-long)
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| Challenge: | Neural Language Models (LMs) trained on large generic training sets have been shown to be effective at adapting to smaller, specific target domains for language modeling and other downstream tasks. |
| Approach: | They propose a framework for a Neural Language Models (LM) to be presented in a common framework. |
| Outcome: | The proposed framework highlights similarities and subtle differences between adaptation techniques and the framework. |
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. |
Lexicosyntactic Inference in Neural Models (D18-1)
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| Challenge: | lexicosyntactic inferences are triggered by surprising aspects of the syntactical context that a word occurs in. |
| Approach: | They build a factuality judgment dataset for English clause-embedding verbs in various syntactic contexts and use it to probe the behavior of current state-of-the-art neural systems. |
| Outcome: | The proposed model makes systematic errors that are visible through the lens of factuality prediction. |
Language Adaptation of Large Language Models: An Empirical Study on LLaMA2 (2025.coling-main)
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| Challenge: | Popularity of Large Language Models (LLMs) has seen a skyrocketing increase in recent years. |
| Approach: | They present a systematic review of the language adaptation process for Large Language Models including vocabulary expansion, continued pre-training, and instruction fine-tuning. |
| Outcome: | The proposed model is based on empirical studies conducted on LLaMA2 and discussions on various settings affecting the model's capabilities. |
Contextualized Word Representations for Reading Comprehension (N18-2)
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| Challenge: | Reading comprehension (RC) is a high-level task in natural language understanding that requires reading a document and answering questions about its content. |
| Approach: | They propose to provide a standard neural network for reading a document and answering a question about its content. |
| Outcome: | The proposed model improves on the competitive SQuAD dataset by providing rich contextualized word representations and allowing it to choose between context-dependent and context-independent representations. |
Quantifying Adaptability in Pre-trained Language Models with 500 Tasks (2022.naacl-main)
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| Challenge: | a recent study examines the features and limits of LM adaptability to new tasks . many questions about the nature and limits remain unanswered . |
| Approach: | They evaluate adaptability to new tasks using a new benchmark, TaskBench500 . they find adaptation procedures differ dramatically in their ability to memorize small datasets . |
| Outcome: | The proposed benchmark compares 500 procedurally generated sequence modeling tasks to a new benchmark. |