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    <abstract>Web-scale text corpora provide NLP researchers and corpus linguists with access to vast quantities of language data, but efficiently identifying the subsets of a large corpus that are relevant to a particular research aim remains a significant challenge. Existing approaches, including keyword search and task-specific classifiers, are often either too restrictive or depend on costly retraining for each new research question. We explore and evaluate the feasibility of a task-agnostic, LLM-driven descriptor schema for annotating web documents, using LLMs to generate natural-language labels that capture stylistic, tonal, topical, and structural properties of texts. Unlike prior work that emphasizes broad categorization or quality filtering, our approach yields fine-grained and interpretable descriptors designed to support flexible downstream applications. We evaluate the approach from multiple angles and show that descriptor-based annotation can enable flexible document retrieval while also exposing challenges related to schema harmonization and descriptor specificity.</abstract>
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%0 Conference Proceedings
%T Task-Agnostic Web Document Annotation with LLM-Generated Descriptors
%A Tarkka, Otto
%A Henriksson, Erik
%A Kanerva, Jenna
%A Ginter, Filip
%Y Ortiz Suarez, Pedro
%Y Burchell, Laurie
%Y Ljubešić, Nikola
%Y Scherrer, Yves
%Y Laippala, Veronika
%Y Dinić, Vuk
%Y Vaughan, Thom
%S Proceedings of the 13th Web-as-Corpus Workshop
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-481-1
%F tarkka-etal-2026-task
%X Web-scale text corpora provide NLP researchers and corpus linguists with access to vast quantities of language data, but efficiently identifying the subsets of a large corpus that are relevant to a particular research aim remains a significant challenge. Existing approaches, including keyword search and task-specific classifiers, are often either too restrictive or depend on costly retraining for each new research question. We explore and evaluate the feasibility of a task-agnostic, LLM-driven descriptor schema for annotating web documents, using LLMs to generate natural-language labels that capture stylistic, tonal, topical, and structural properties of texts. Unlike prior work that emphasizes broad categorization or quality filtering, our approach yields fine-grained and interpretable descriptors designed to support flexible downstream applications. We evaluate the approach from multiple angles and show that descriptor-based annotation can enable flexible document retrieval while also exposing challenges related to schema harmonization and descriptor specificity.
%U https://aclanthology.org/2026.wac-1.24/
%P 252-281

Markdown (Informal)

[Task-Agnostic Web Document Annotation with LLM-Generated Descriptors](https://aclanthology.org/2026.wac-1.24/) (Tarkka et al., WAC 2026)

• Task-Agnostic Web Document Annotation with LLM-Generated Descriptors (Tarkka et al., WAC 2026)

ACL

• Otto Tarkka, Erik Henriksson, Jenna Kanerva, and Filip Ginter. 2026. Task-Agnostic Web Document Annotation with LLM-Generated Descriptors. In Proceedings of the 13th Web-as-Corpus Workshop, pages 252–281, Budapest, Hungary. Association for Computational Linguistics.