@inproceedings{yeen-etal-2025-manta,
title = "{MANTA}: A Scalable Pipeline for Transmuting Massive Web Corpora into Instruction Datasets",
author = "Yeen, Heuiyeen and
Hong, Seokhee and
Yun, Hyeongu and
Lee, Jinsik",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1019/",
pages = "18755--18770",
ISBN = "979-8-89176-335-7",
abstract = "We introduce MANTA, an automated pipeline that generates high-quality large-scale instruction fine-tuning datasets from massive web corpora while preserving their diversity and scalability. By extracting structured syllabi from web documents and leveraging high-performance LLMs, our approach enables highly effective query-response generation with minimal human intervention. Extensive experiments on 8B-scale LLMs demonstrate that fine-tuning on the MANTA-1M dataset significantly outperforms other massive dataset generation methodologies, particularly in knowledge-intensive tasks such as MMLU and MMLU-Pro, while also delivering superior performance across a broad spectrum of tasks. Moreover, MANTA supports seamless scalability by allowing the continuous integration of web corpus data, enabling expansion into domains requiring intensive knowledge."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yeen-etal-2025-manta">
<titleInfo>
<title>MANTA: A Scalable Pipeline for Transmuting Massive Web Corpora into Instruction Datasets</title>
</titleInfo>
<name type="personal">
<namePart type="given">Heuiyeen</namePart>
<namePart type="family">Yeen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Seokhee</namePart>
<namePart type="family">Hong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hyeongu</namePart>
<namePart type="family">Yun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jinsik</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2025</title>
</titleInfo>
<name type="personal">
<namePart type="given">Christos</namePart>
<namePart type="family">Christodoulopoulos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tanmoy</namePart>
<namePart type="family">Chakraborty</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Carolyn</namePart>
<namePart type="family">Rose</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Violet</namePart>
<namePart type="family">Peng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Suzhou, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-335-7</identifier>
</relatedItem>
<abstract>We introduce MANTA, an automated pipeline that generates high-quality large-scale instruction fine-tuning datasets from massive web corpora while preserving their diversity and scalability. By extracting structured syllabi from web documents and leveraging high-performance LLMs, our approach enables highly effective query-response generation with minimal human intervention. Extensive experiments on 8B-scale LLMs demonstrate that fine-tuning on the MANTA-1M dataset significantly outperforms other massive dataset generation methodologies, particularly in knowledge-intensive tasks such as MMLU and MMLU-Pro, while also delivering superior performance across a broad spectrum of tasks. Moreover, MANTA supports seamless scalability by allowing the continuous integration of web corpus data, enabling expansion into domains requiring intensive knowledge.</abstract>
<identifier type="citekey">yeen-etal-2025-manta</identifier>
<location>
<url>https://aclanthology.org/2025.findings-emnlp.1019/</url>
</location>
<part>
<date>2025-11</date>
<extent unit="page">
<start>18755</start>
<end>18770</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T MANTA: A Scalable Pipeline for Transmuting Massive Web Corpora into Instruction Datasets
%A Yeen, Heuiyeen
%A Hong, Seokhee
%A Yun, Hyeongu
%A Lee, Jinsik
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F yeen-etal-2025-manta
%X We introduce MANTA, an automated pipeline that generates high-quality large-scale instruction fine-tuning datasets from massive web corpora while preserving their diversity and scalability. By extracting structured syllabi from web documents and leveraging high-performance LLMs, our approach enables highly effective query-response generation with minimal human intervention. Extensive experiments on 8B-scale LLMs demonstrate that fine-tuning on the MANTA-1M dataset significantly outperforms other massive dataset generation methodologies, particularly in knowledge-intensive tasks such as MMLU and MMLU-Pro, while also delivering superior performance across a broad spectrum of tasks. Moreover, MANTA supports seamless scalability by allowing the continuous integration of web corpus data, enabling expansion into domains requiring intensive knowledge.
%U https://aclanthology.org/2025.findings-emnlp.1019/
%P 18755-18770
Markdown (Informal)
[MANTA: A Scalable Pipeline for Transmuting Massive Web Corpora into Instruction Datasets](https://aclanthology.org/2025.findings-emnlp.1019/) (Yeen et al., Findings 2025)
ACL