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Strong machine-translation benchmark scores do not by themselves show whether translation tools help people communicate. Marine Carpuat presents human-centered research on how nonexpert users understand and rely on machine translation, and draws implications for evaluation and system design. The talk then considers open research questions about making successful communication a central objective of large-language-model research and development.
Tue, Nov 3 · 19:00 UTC · Online
Human recall of meaningful narratives suggests that comprehension builds a hierarchical memory representation. This talk presents the recall evidence, a mathematical model reproducing the observed organization, and language-model-assisted semantic segmentation supporting a related hierarchical account of language.
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Large speech and language models offer testable algorithmic hypotheses about how the brain understands language. Laura Gwilliams examines neural recordings at several spatial scales, from population activity measured with MEG and intracranial EEG to individual neurons sampled across cortical depth with human Neuropixels probes. The studies ask which representations and operations connect speech sounds to meaning, and how information at different timescales is nested across time and cortical space. Comparing biological recordings with artificial models reveals candidate mechanisms for exchanging information across hierarchical structures and for implementing language understanding at the level of whole neural systems.
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Small language models trained on limited data can provide economical, controlled simulations for studying human language acquisition. Alex Warstadt discusses the BabyLM Challenge and its goals of improving data efficiency, cognitive plausibility and accessibility. Studies from his UC San Diego lab train models from scratch under controlled conditions to investigate critical periods in second-language learning, inductive biases behind cross-linguistic patterns, and distributional signatures of word-learning trajectories. Comparing cases where these models resemble human learners with cases where they diverge helps locate human language acquisition within a broader space of possible learning systems. The approach supports fundamental work across machine learning, linguistics and cognitive science.
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What can fluent language models tell us about linguistic structure and human language learning? Kyle Mahowald evaluates two opposing positions: that models do not learn language in a way relevant to people, and that their success makes linguistic theory unnecessary. Drawing on a position paper with Richard Futrell, he argues that language models provide useful evidence about structure, processing and acquisition while leaving an essential role for theory. The talk examines grammaticality judgments in language models and controlled pretraining experiments in which small models learn from systematically manipulated corpora. These methods allow researchers to revisit claims about learning and identify which properties of the input support linguistic generalisation.
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Executive functions are cognitive processes that allow us to plan, monitor and execute our goals. Using fMRI, we investigated how early deafness influences crossmodal plasticity and the organisation of executive functions in the adult human brain. Results from a range of visual executive function tasks (working memory, task switching, planning, inhibition) show that deaf individuals specifically recruit superior temporal “auditory” regions during task switching. Neural activity in auditory regions predicts behavioural performance during task switching in deaf individuals, highlighting the functional relevance of the observed cortical reorganisation. Furthermore, language grammatical skills were correlated with the level of activation and functional connectivity of fronto-parietal networks. Together, these findings show the interplay between sensory and language experience in the organisation of executive processing in the brain.
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Metaphor is a pervasive phenomenon in language and cognition. To date, the vast majority of psycholinguistic research on metaphor has focused on noun-noun metaphors of the form An X is a Y (e.g., My job is a jail). Yet there is evidence that verb metaphor (e.g., I sailed through my exams) is more common. Despite this, comparatively little work has examined how verb metaphors are processed. In this talk, I will propose a novel account for verb metaphor comprehension: verb metaphors are understood in the same way that analogies are—as comparisons processed via structure-mapping. I will discuss the predictions that arise from applying the analogical framework to verb metaphor and present a series of experiments showing that verb metaphoric extension is consistent with those predictions.
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