@inproceedings{tamine-etal-2026-atomic,
title = "From Atomic to Complex tasks: Cross-Tasking Improves Zero-Shot Argument Generation and Retrieval",
author = "Tamine, Lynda and
Kebir, Ahmed Rayane and
Codjo, Merveille Dona and
Pasquies, Enzo and
Moreno, Jose G",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1246/",
pages = "24864--24882",
ISBN = "979-8-89176-395-1",
abstract = "Cross-task generalization mimics human intelligence through the ability to perform tasks by recalling foundational skills acquired previously. In this paper, we argue that argument generation and argument retrieval are complex tasks that could leverage cross-tasking atomic argument mining and argument quality assessment tasks, even if there is no supervision. We empirically demonstrate the rationale behind our claim through the $\textit{ArgLLM}$ framework, including a total of 18.9K instruction data using a multi-choice question-answering format, scaling up through multi-tasking and model merging, six natural language argumentation atomic tasks to four complex argument generation and argument retrieval tasks. Our results and analysis, using the backbone Mistral and Llama models, show that cross-tasking in zero-shot settings outperforms base models and is robust to varying strategies, tasks, and model sizes, offering a valuable trade-off between computational cost and task performance."
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<abstract>Cross-task generalization mimics human intelligence through the ability to perform tasks by recalling foundational skills acquired previously. In this paper, we argue that argument generation and argument retrieval are complex tasks that could leverage cross-tasking atomic argument mining and argument quality assessment tasks, even if there is no supervision. We empirically demonstrate the rationale behind our claim through the ArgLLM framework, including a total of 18.9K instruction data using a multi-choice question-answering format, scaling up through multi-tasking and model merging, six natural language argumentation atomic tasks to four complex argument generation and argument retrieval tasks. Our results and analysis, using the backbone Mistral and Llama models, show that cross-tasking in zero-shot settings outperforms base models and is robust to varying strategies, tasks, and model sizes, offering a valuable trade-off between computational cost and task performance.</abstract>
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%0 Conference Proceedings
%T From Atomic to Complex tasks: Cross-Tasking Improves Zero-Shot Argument Generation and Retrieval
%A Tamine, Lynda
%A Kebir, Ahmed Rayane
%A Codjo, Merveille Dona
%A Pasquies, Enzo
%A Moreno, Jose G.
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F tamine-etal-2026-atomic
%X Cross-task generalization mimics human intelligence through the ability to perform tasks by recalling foundational skills acquired previously. In this paper, we argue that argument generation and argument retrieval are complex tasks that could leverage cross-tasking atomic argument mining and argument quality assessment tasks, even if there is no supervision. We empirically demonstrate the rationale behind our claim through the ArgLLM framework, including a total of 18.9K instruction data using a multi-choice question-answering format, scaling up through multi-tasking and model merging, six natural language argumentation atomic tasks to four complex argument generation and argument retrieval tasks. Our results and analysis, using the backbone Mistral and Llama models, show that cross-tasking in zero-shot settings outperforms base models and is robust to varying strategies, tasks, and model sizes, offering a valuable trade-off between computational cost and task performance.
%U https://aclanthology.org/2026.findings-acl.1246/
%P 24864-24882
Markdown (Informal)
[From Atomic to Complex tasks: Cross-Tasking Improves Zero-Shot Argument Generation and Retrieval](https://aclanthology.org/2026.findings-acl.1246/) (Tamine et al., Findings 2026)
ACL