@inproceedings{eljadiri-nurbakova-2025-team,
title = "Team {INSAL}yon2 at {S}em{E}val-2025 Task 10: A Zero-shot Agentic Approach to Text Classification",
author = "Eljadiri, Mohamed - Nour and
Nurbakova, Diana",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.129/",
pages = "965--980",
ISBN = "979-8-89176-273-2",
abstract = "We present Team INSALyon2{'}s agentic approach to SemEval-2025 Task 10 Subtask 2, which focuses on the multi-label classification of narratives in news articles across five languages. Our system employs a zero-shot architecture where specialized Large Language Model (LLM) agents handle binary classification tasks for individual narrative/subnarrative labels, with a meta-agent aggregating these decisions into final multi-label predictions. Instead of fine-tuning on the dataset, we leverage AutoGen to orchestrate multiple GPT-based agents, each responsible for detecting specific narrative/subnarrative types in a modular framework. This agent-based approach naturally handles the challenge of multi-label classification by enabling parallel decisions across the two-level taxonomy. Experiments on the English subset demonstrate strong performance with our system achieving F1{\_}macro{\_}coarse = 0.513, F1{\_}sample = 0.406, securing third place in the competition. Our findings show that zero-shot agentic approaches can be competitive in complex classification tasks."
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<abstract>We present Team INSALyon2’s agentic approach to SemEval-2025 Task 10 Subtask 2, which focuses on the multi-label classification of narratives in news articles across five languages. Our system employs a zero-shot architecture where specialized Large Language Model (LLM) agents handle binary classification tasks for individual narrative/subnarrative labels, with a meta-agent aggregating these decisions into final multi-label predictions. Instead of fine-tuning on the dataset, we leverage AutoGen to orchestrate multiple GPT-based agents, each responsible for detecting specific narrative/subnarrative types in a modular framework. This agent-based approach naturally handles the challenge of multi-label classification by enabling parallel decisions across the two-level taxonomy. Experiments on the English subset demonstrate strong performance with our system achieving F1_macro_coarse = 0.513, F1_sample = 0.406, securing third place in the competition. Our findings show that zero-shot agentic approaches can be competitive in complex classification tasks.</abstract>
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%0 Conference Proceedings
%T Team INSALyon2 at SemEval-2025 Task 10: A Zero-shot Agentic Approach to Text Classification
%A Eljadiri, Mohamed -. Nour
%A Nurbakova, Diana
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F eljadiri-nurbakova-2025-team
%X We present Team INSALyon2’s agentic approach to SemEval-2025 Task 10 Subtask 2, which focuses on the multi-label classification of narratives in news articles across five languages. Our system employs a zero-shot architecture where specialized Large Language Model (LLM) agents handle binary classification tasks for individual narrative/subnarrative labels, with a meta-agent aggregating these decisions into final multi-label predictions. Instead of fine-tuning on the dataset, we leverage AutoGen to orchestrate multiple GPT-based agents, each responsible for detecting specific narrative/subnarrative types in a modular framework. This agent-based approach naturally handles the challenge of multi-label classification by enabling parallel decisions across the two-level taxonomy. Experiments on the English subset demonstrate strong performance with our system achieving F1_macro_coarse = 0.513, F1_sample = 0.406, securing third place in the competition. Our findings show that zero-shot agentic approaches can be competitive in complex classification tasks.
%U https://aclanthology.org/2025.semeval-1.129/
%P 965-980
Markdown (Informal)
[Team INSALyon2 at SemEval-2025 Task 10: A Zero-shot Agentic Approach to Text Classification](https://aclanthology.org/2025.semeval-1.129/) (Eljadiri & Nurbakova, SemEval 2025)
ACL