@inproceedings{bi-etal-2026-prism,
title = "{PRISM}: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection",
author = "Bi, Jinhe and
Aniri and
Jin, Zengjie and
Wang, Yifan and
Yan, Danqi and
Huang, Wenke and
Ma, Xiaowen and
Yan, Sikuan and
Hecker, Artur and
Ye, Mang and
Xiao, Xun and
Schuetze, Hinrich and
Tresp, Volker and
Ma, Yunpu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.633/",
pages = "14557--14580",
ISBN = "979-8-89176-459-0",
abstract = "Visual instruction tuning adapts pre-trained Multimodal Large Language Models (MLLMs) to follow human instructions for real-world applications. However, rapidly growing instruction datasets introduce substantial redundancy and computational costs. Existing data selection methods aim to prune this redundancy, but predominantly rely on computationally demanding proxy-based inference or training-based metrics, often exacerbating the efficiency bottlenecks they are intended to resolve. To address this challenge, we identify a critical yet overlooked factor: anisotropy in visual feature distributions. We find that this anisotropy induces a Global Semantic Drift that distorts similarity estimates and limits efficient data selection. Motivated by this insight, we devise PRISM, a training-free framework for efficient visual instruction selection. PRISM mitigates the dominant global drift through implicit re-centering, allowing intrinsic visual semantics to provide a reliable redundancy signal without proxy models or additional training. Empirically, PRISM reduces end-to-end GPU time by about 70{\%} while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across multimodal and language understanding benchmarks and reaching 101.7{\%} relative performance against the full-data baseline."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bi-etal-2026-prism">
<titleInfo>
<title>PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jinhe</namePart>
<namePart type="family">Bi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name>
<namePart>Aniri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zengjie</namePart>
<namePart type="family">Jin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yifan</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Danqi</namePart>
<namePart type="family">Yan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wenke</namePart>
<namePart type="family">Huang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xiaowen</namePart>
<namePart type="family">Ma</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sikuan</namePart>
<namePart type="family">Yan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Artur</namePart>
<namePart type="family">Hecker</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mang</namePart>
<namePart type="family">Ye</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xun</namePart>
<namePart type="family">Xiao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hinrich</namePart>
<namePart type="family">Schuetze</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Volker</namePart>
<namePart type="family">Tresp</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yunpu</namePart>
<namePart type="family">Ma</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-459-0</identifier>
</relatedItem>
<abstract>Visual instruction tuning adapts pre-trained Multimodal Large Language Models (MLLMs) to follow human instructions for real-world applications. However, rapidly growing instruction datasets introduce substantial redundancy and computational costs. Existing data selection methods aim to prune this redundancy, but predominantly rely on computationally demanding proxy-based inference or training-based metrics, often exacerbating the efficiency bottlenecks they are intended to resolve. To address this challenge, we identify a critical yet overlooked factor: anisotropy in visual feature distributions. We find that this anisotropy induces a Global Semantic Drift that distorts similarity estimates and limits efficient data selection. Motivated by this insight, we devise PRISM, a training-free framework for efficient visual instruction selection. PRISM mitigates the dominant global drift through implicit re-centering, allowing intrinsic visual semantics to provide a reliable redundancy signal without proxy models or additional training. Empirically, PRISM reduces end-to-end GPU time by about 70% while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across multimodal and language understanding benchmarks and reaching 101.7% relative performance against the full-data baseline.</abstract>
<identifier type="citekey">bi-etal-2026-prism</identifier>
<location>
<url>https://aclanthology.org/2026.emnlp-main.633/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>14557</start>
<end>14580</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection
%A Bi, Jinhe
%A Jin, Zengjie
%A Wang, Yifan
%A Yan, Danqi
%A Huang, Wenke
%A Ma, Xiaowen
%A Yan, Sikuan
%A Hecker, Artur
%A Ye, Mang
%A Xiao, Xun
%A Schuetze, Hinrich
%A Tresp, Volker
%A Ma, Yunpu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%A Aniri
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F bi-etal-2026-prism
%X Visual instruction tuning adapts pre-trained Multimodal Large Language Models (MLLMs) to follow human instructions for real-world applications. However, rapidly growing instruction datasets introduce substantial redundancy and computational costs. Existing data selection methods aim to prune this redundancy, but predominantly rely on computationally demanding proxy-based inference or training-based metrics, often exacerbating the efficiency bottlenecks they are intended to resolve. To address this challenge, we identify a critical yet overlooked factor: anisotropy in visual feature distributions. We find that this anisotropy induces a Global Semantic Drift that distorts similarity estimates and limits efficient data selection. Motivated by this insight, we devise PRISM, a training-free framework for efficient visual instruction selection. PRISM mitigates the dominant global drift through implicit re-centering, allowing intrinsic visual semantics to provide a reliable redundancy signal without proxy models or additional training. Empirically, PRISM reduces end-to-end GPU time by about 70% while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across multimodal and language understanding benchmarks and reaching 101.7% relative performance against the full-data baseline.
%U https://aclanthology.org/2026.emnlp-main.633/
%P 14557-14580
Markdown (Informal)
[PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection](https://aclanthology.org/2026.emnlp-main.633/) (Bi et al., EMNLP 2026)
ACL
- Jinhe Bi, Aniri, Zengjie Jin, Yifan Wang, Danqi Yan, Wenke Huang, Xiaowen Ma, Sikuan Yan, Artur Hecker, Mang Ye, Xun Xiao, Hinrich Schuetze, Volker Tresp, and Yunpu Ma. 2026. PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 14557–14580, Budapest, Hungary. Association for Computational Linguistics.