Hearing Loss Alters the Subcellular Distribution of Presynaptic GAD and Postsynaptic GABAA Receptors in the Auditory Cortex

We have shown previously that auditory experience regulates the maturation of excitatory synapses in the auditory cortex (ACx). In this study, we used electron microscopic immunocytochemistry to determine whether the heightened excitability of the ACx following neonatal sensorineural hearing loss (SNHL) also involves pre- or postsynaptic alterations of GABAergic synapses. SNHL was induced in gerbils just prior to the onset of hearing (postnatal day 10). At P17, the gamma-aminobutyri acid type A (GABAA) receptor's β2/3-subunit (GABAAβ2/3) clusters residing at plasma membranes in layers 2/3 of ACx was reduced significantly in size (P < 0.05) and number (P < 0.005), whereas the overall number of immunoreactive puncta (intracellular + plasmalemmal) remained unchanged. The reduction of GABAAβ2/3 was observed along perikaryal plasma membranes of excitatory neurons but not of GABAergic interneurons. This cell-specific change can contribute to the enhanced excitability of SNHL ACx. Presynaptically, GABAergic axon terminals were significantly larger but less numerous and contained 47% greater density of glutamic acid decarboxylase immunoreactivity (P < 0.05). This suggests that GABA synthesis may be upregulated by a retrograde signal arising from lowered levels of postsynaptic GABAAR. Thus, both, the pre- and postsynaptic sides of inhibitory synapses that form upon pyramidal neurons of the ACx are regulated by neonatal auditory experience

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc