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Faith and fate: Limits of transformers on compositionality
Transformer large language models (LLMs) have sparked admiration for their exceptional
performance on tasks that demand intricate multi-step reasoning. Yet, these models …
performance on tasks that demand intricate multi-step reasoning. Yet, these models …
Exploring length generalization in large language models
The ability to extrapolate from short problem instances to longer ones is an important form of
out-of-distribution generalization in reasoning tasks, and is crucial when learning from …
out-of-distribution generalization in reasoning tasks, and is crucial when learning from …
A taxonomy and review of generalization research in NLP
The ability to generalize well is one of the primary desiderata for models of natural language
processing (NLP), but what 'good generalization'entails and how it should be evaluated is …
processing (NLP), but what 'good generalization'entails and how it should be evaluated is …
Foundation models for music: A survey
In recent years, foundation models (FMs) such as large language models (LLMs) and latent
diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This …
diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This …
Efficient methods for natural language processing: A survey
Recent work in natural language processing (NLP) has yielded appealing results from
scaling model parameters and training data; however, using only scale to improve …
scaling model parameters and training data; however, using only scale to improve …
Compositionality decomposed: How do neural networks generalise?
Despite a multitude of empirical studies, little consensus exists on whether neural networks
are able to generalise compositionally, a controversy that, in part, stems from a lack of …
are able to generalise compositionally, a controversy that, in part, stems from a lack of …
Transformers can achieve length generalization but not robustly
Length generalization, defined as the ability to extrapolate from shorter training sequences
to longer test ones, is a significant challenge for language models. This issue persists even …
to longer test ones, is a significant challenge for language models. This issue persists even …
Functional interpolation for relative positions improves long context transformers
Preventing the performance decay of Transformers on inputs longer than those used for
training has been an important challenge in extending the context length of these models …
training has been an important challenge in extending the context length of these models …
State-of-the-art generalisation research in NLP: a taxonomy and review
The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …
Length generalization in arithmetic transformers
We examine how transformers cope with two challenges: learning basic integer arithmetic,
and generalizing to longer sequences than seen during training. We find that relative …
and generalizing to longer sequences than seen during training. We find that relative …