Summary Scaling Multilingual Corpora and Language Models arxiv.org
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The authors suggest horizontally scaling Large Language Models (LLMs) for low-resource languages and demonstrate this through the creation of Glot500-m, while also examining transfer learning and benchmarking dialectal variations.
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Key Points
- The NLP community has focused on scaling Large Language Models (LLMs) vertically for high-resource languages.
- This paper proposes scaling LLMs horizontally to a large number of predominantly low-resource languages with Glot500-m.
- Glot500-m is a multilingual model trained on a 600GB corpus covering over 500 diverse languages.
- Glot500-m outperforms XLM-R-B on various language tasks for both head and tail language-scripts, except for POS on head.
- Glot500-m performs better for languages it was pretrained on, but can also improve performance for languages not covered by XLM-R if enough data is collected.
Summaries
31 word summary
The NLP community has focused on scaling Large Language Models (LLMs) vertically, but the authors propose scaling horizontally for low-resource languages. They create Glot500-m and explore transfer learning and benchmarking dialectal.
77 word summary
The NLP community has primarily focused on scaling Large Language Models (LLMs) vertically for high-resource languages. However, the authors propose scaling LLMs horizontally to a large number of predominantly low-resource languages. They create Glot500-m, a
This summary discusses the main points from the text excerpt on scaling multilingual corpora and language models. The excerpt mentions research papers and conference proceedings related to multilingual language models and natural language processing, exploring topics such as transfer learning, benchmarking dialectal
925 word summary
The NLP community has primarily focused on scaling Large Language Models (LLMs) vertically for high-resource languages. In this paper, the authors propose scaling LLMs horizontally to a large number of predominantly low-resource languages. They create Glot500-m
The curse of multilinguality has been studied for high-resource languages, but Glot500-m allows for investigation in a more realistic setting. Glot500-m is a multilingual model trained on a 600GB corpus covering over 500 diverse languages
The article discusses the scaling of multilingual corpora and language models. It mentions that some languages are written in multiple scripts, and each language-script is treated as a separate entity. A 3-gram character-level language model is trained for each language
We merge tokens with XLM-R's vocabulary, adding 100K new tokens. The probabilities of genuinely new tokens are taken from SentencePiece. The new tokenizer changes 0.2% to 50% of tokens in head languages, but this
We compare Glot500-m and XLM-R-B in various language tasks. Glot500-m supports 354 language-scripts and outperforms XLM-R-B on all tasks for both head and tail language-scripts, except for POS on head.
Glot500-m outperforms XLM-R-B in terms of pseudoperplexity, particularly for tail language-scripts. The training progress of Glot500-m shows rapid improvement at the beginning but slows down later, especially for tail languages. Gl
Glot500-m performs better for languages it was pretrained on, but can also improve performance for languages not covered by XLM-R if enough data is collected. The difference in coverage between Glot500-m and XLM-R is partially predictive of performance
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Composable sparse fine-tuning for cross-lingual transfer. Massively multilingual sentence embeddings for zero-shot cross-lingual transfer. Empirical models for an indic language continuum. ParaCrawl: Web-scale acquisition of parallel corpora. Mac
This summary provides a list of references from various papers and conferences related to the topic of scaling multilingual corpora and language models. The references include papers on cross-lingual language model pre-training, investigating language relationships in multilingual sentence encoders,
Mapping languages: the corpus of global language use. Ethnologue: Languages of the world. How to adapt pretrained multilingual models to 1600 languages. Habibi - a multi dialect multi national Arabic song lyrics corpus. Arabic dialect identification in the
The summary is as follows:
- Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages, Dublin, Ireland. - Many-to-English machine translation tools, data, and pretrained models. - Xl-sum:
Taku Kudo and John Richardson presented SentencePiece, a subword tokenizer for neural text processing. Anoop Kunchukuttan, Pratik Mehta, and Pushpak Bhattacharyya introduced the IIT Bombay English-Hindi parallel
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This excerpt includes references to various research papers and conference proceedings related to multilingual language models and natural language processing. The mentioned works explore topics such as transfer learning, benchmarking dialectal Arabic-English machine translation, masked language model scoring, parallel sentence mining
This summary provides a concise version of the text excerpt while retaining important details and highlighting key points. The summary is organized into separate paragraphs to distinguish distinct ideas for readability, while preserving the original order in which ideas were presented.
The text excerpt includes references to
Perplexity is used to measure how well a language model predicts test data. The divergence between two languages is computed using the maximum perplexity values in both directions. The study evaluates the proposed approach using language family trees as a baseline. The accuracy of
The document discusses scaling multilingual corpora and language models. It mentions various tools and resources used in the study, including Head and various language models. The detailed results for different tasks and languages are reported in tables. Perplexity numbers for all languages
The text excerpt contains a list of numerical values and language-script combinations. The values in the list are not explained or described, making it difficult to understand their meaning. The language-script combinations represent different languages and writing systems. The purpose or context of this
The text excerpt consists of a list of language-script pairs followed by numerical values. The pairs represent different languages written in different scripts, and the numerical values denote accuracy scores for three different models. The accuracy scores indicate the performance of the models on a sentence
Table 17 shows the F1 scores of XLM-R-B, XLM-R-L, and Glot500-m on NER. The scores are listed for various language-scripts, such as ori-Orya, oss-Cyrl, pan
The excerpt consists of a long list of language-script combinations and their corresponding F1 scores for XLM-R-B, XLM-R-L, and Glot500-m language models on text classification. The list includes various language scripts such as Latin, Cyril
The summary provides a list of language-script pairs along with their respective F1 scores for XLM-R-B, XLM-R-L, and Glot500-m language models in text classification. The language-script pairs are organized in a table format, with
The excerpt includes a long list of numerical values and language-script pairs. The accuracy of different language models (XLM-R-B, XLM-R-L, and Glot500-m) in round trip alignment is provided for each language-script pair.
The excerpt presents a table showing the accuracy of XLM-R-B, XLM-R-L, and Glot500-m on Round Trip Alignment. The table includes language-script pairs and corresponding accuracy scores. The language-script pairs are listed in the first column
The document provides a table showing perplexity values for various languages covered by Glot500-m. The table includes language-script pairs, as well as perplexity scores for two language models (XLM-R-B and XLM-R-L) and the Gl
Perplexity scores for various languages in the Glot500-m dataset are provided in Tables 24 and 25. The tables include language-script pairs, perplexity scores for the XLM-R-B and XLM-R-L language models, and the