Add to ORKGLanguage models (LMs) have been used in cognitive modeling as well as engineering studies -- they compute information-theoretic complexity metrics that simulate humans' cognitive load during reading. This study highlights a limitation of modern neural LMs as the model of choice for this purpose: there is a discrepancy between their context access capacities and that of humans. Our results showed that constraining the LMs' context access improved their simulation of human reading behavior. We also showed that LM-human gaps in context access were associated with specific syntactic constructions; incorporating syntactic biases into LMs' context access might enhance their cognitive plausibility.Comment: Accepted by EMNLP2022 (main long
Neural Language Models (NLMs) have made tremendous advances during the last years, achieving impress...
While recent language models have the ability to take long contexts as input, relatively little is k...
International audienceSentence comprehension requires inferring, from a sequence of words, the struc...
This paper explores the relationship between Neural Language Model (NLM) perplexity and sentence rea...
We present a targeted, scaled-up comparison of incremental processing in humans and neural language ...
Large language models (LLMs) exhibit remarkable performance improvement through in-context learning ...
The increasingly widespread adoption of large language models has highlighted the need for improving...
In computational psycholinguistics, various language models have been evaluated against human readin...
Large language models such as ChatGPT are deep learning architectures trained on immense quantities ...
It has been shown that complexity metrics, computed by a syntactic parser, is a predictor of human r...
Establishing whether language models can use contextual information in a human-plausible way is impo...
Large Language Models (LLMs) such as ChatGPT are deep learning architectures that have been trained ...
260 pagesThe majority of work at the intersection of computational linguistics and natural language ...
International audienceNeural Language Models (NLMs) have made tremendous advances during the last ye...
This paper investigates the relationship between two complementary perspectives in the human assessm...
Neural Language Models (NLMs) have made tremendous advances during the last years, achieving impress...
While recent language models have the ability to take long contexts as input, relatively little is k...
International audienceSentence comprehension requires inferring, from a sequence of words, the struc...
This paper explores the relationship between Neural Language Model (NLM) perplexity and sentence rea...
We present a targeted, scaled-up comparison of incremental processing in humans and neural language ...
Large language models (LLMs) exhibit remarkable performance improvement through in-context learning ...
The increasingly widespread adoption of large language models has highlighted the need for improving...
In computational psycholinguistics, various language models have been evaluated against human readin...
Large language models such as ChatGPT are deep learning architectures trained on immense quantities ...
It has been shown that complexity metrics, computed by a syntactic parser, is a predictor of human r...
Establishing whether language models can use contextual information in a human-plausible way is impo...
Large Language Models (LLMs) such as ChatGPT are deep learning architectures that have been trained ...
260 pagesThe majority of work at the intersection of computational linguistics and natural language ...
International audienceNeural Language Models (NLMs) have made tremendous advances during the last ye...
This paper investigates the relationship between two complementary perspectives in the human assessm...
Neural Language Models (NLMs) have made tremendous advances during the last years, achieving impress...
While recent language models have the ability to take long contexts as input, relatively little is k...
International audienceSentence comprehension requires inferring, from a sequence of words, the struc...