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        <title>Extending Input Contexts of Language Models through Training on Segmented Sequences</title>
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            <namePart type="given">Helena</namePart>
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    <abstract>Effectively training language models on longinputs poses many technical challenges. As acost consideration, languages models are pre-trained on a fixed sequence length before beingadapted to longer sequences. We explore var-ious methods for adapting models to longerinputs by training on segmented sequences andan interpolation-based method for extendingabsolute positional embeddings. We developa training procedure to extend the input con-text size of pretrained models with no architec-tural changes and no additional memory coststhan training on the original input lengths. Bysub-sampling segments from long inputs whilemaintaining their original position the model isable to learn new positional interactions. Ourmethod benefits both models trained with abso-lute positional embeddings, by extending theirinput contexts, as well as popular relative posi-tional embedding methods showing a reducedperplexity on sequences longer than they weretrained on. We demonstrate our method canextend input contexts by a factor of 4\times whileimproving perplexity.</abstract>
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        <url>https://aclanthology.org/2024.findings-naacl.191</url>
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%0 Conference Proceedings
%T Extending Input Contexts of Language Models through Training on Segmented Sequences
%A Karypis, Petros
%A McAuley, Julian
%A Karypis, George
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F karypis-etal-2024-extending
%X Effectively training language models on longinputs poses many technical challenges. As acost consideration, languages models are pre-trained on a fixed sequence length before beingadapted to longer sequences. We explore var-ious methods for adapting models to longerinputs by training on segmented sequences andan interpolation-based method for extendingabsolute positional embeddings. We developa training procedure to extend the input con-text size of pretrained models with no architec-tural changes and no additional memory coststhan training on the original input lengths. Bysub-sampling segments from long inputs whilemaintaining their original position the model isable to learn new positional interactions. Ourmethod benefits both models trained with abso-lute positional embeddings, by extending theirinput contexts, as well as popular relative posi-tional embedding methods showing a reducedperplexity on sequences longer than they weretrained on. We demonstrate our method canextend input contexts by a factor of 4\times whileimproving perplexity.
%U https://aclanthology.org/2024.findings-naacl.191
%P 3040-3052

Markdown (Informal)

[Extending Input Contexts of Language Models through Training on Segmented Sequences](https://aclanthology.org/2024.findings-naacl.191) (Karypis et al., Findings 2024)

• Extending Input Contexts of Language Models through Training on Segmented Sequences (Karypis et al., Findings 2024)

ACL

• Petros Karypis, Julian McAuley, and George Karypis. 2024. Extending Input Contexts of Language Models through Training on Segmented Sequences. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3040–3052, Mexico City, Mexico. Association for Computational Linguistics.