@inproceedings{ashury-tahan-etal-2026-robustness,
title = "Robustness as an Emergent Property of Task Performance",
author = "Ashury-Tahan, Shir and
Gera, Ariel and
Bandel, Elron and
Shmueli-Scheuer, Michal and
Choshen, Leshem",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.138/",
pages = "2847--2869",
ISBN = "979-8-89176-462-0",
abstract = "Robustness is often regarded as a critical future challenge for real-world applications, where stability is essential. However, as models often learn tasks in a similar order, we hypothesize that easier tasks will be easier regardless of how they are presented to the model. Indeed, in this paper, we show that as models approach high performance on a task, robustness to semantics-preserving changes is effectively achieved. Through an empirical analysis of multiple models across diverse datasets and configurations (e.g., paraphrases, different temperatures), we find a strong positive correlation. Moreover, we find that robustness is primarily driven by task-specific competence rather than inherent model-level properties, challenging current approaches that treat robustness as an independent capability. Thus, from a high-level perspective, we may expect that as new tasks saturate, model robustness on these tasks will emerge accordingly. For researchers, this implies that explicit efforts to measure and improve robustness may warrant reduced emphasis, as such robustness is likely to develop alongside performance gains. For practitioners, it acts as a sign that indeed the tasks that the literature deals with are unreliable, but on easier past tasks, the models are reliable and ready for real-world deployment."
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%0 Conference Proceedings
%T Robustness as an Emergent Property of Task Performance
%A Ashury-Tahan, Shir
%A Gera, Ariel
%A Bandel, Elron
%A Shmueli-Scheuer, Michal
%A Choshen, Leshem
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F ashury-tahan-etal-2026-robustness
%X Robustness is often regarded as a critical future challenge for real-world applications, where stability is essential. However, as models often learn tasks in a similar order, we hypothesize that easier tasks will be easier regardless of how they are presented to the model. Indeed, in this paper, we show that as models approach high performance on a task, robustness to semantics-preserving changes is effectively achieved. Through an empirical analysis of multiple models across diverse datasets and configurations (e.g., paraphrases, different temperatures), we find a strong positive correlation. Moreover, we find that robustness is primarily driven by task-specific competence rather than inherent model-level properties, challenging current approaches that treat robustness as an independent capability. Thus, from a high-level perspective, we may expect that as new tasks saturate, model robustness on these tasks will emerge accordingly. For researchers, this implies that explicit efforts to measure and improve robustness may warrant reduced emphasis, as such robustness is likely to develop alongside performance gains. For practitioners, it acts as a sign that indeed the tasks that the literature deals with are unreliable, but on easier past tasks, the models are reliable and ready for real-world deployment.
%U https://aclanthology.org/2026.findings-emnlp.138/
%P 2847-2869
Markdown (Informal)
[Robustness as an Emergent Property of Task Performance](https://aclanthology.org/2026.findings-emnlp.138/) (Ashury-Tahan et al., Findings 2026)
ACL
- Shir Ashury-Tahan, Ariel Gera, Elron Bandel, Michal Shmueli-Scheuer, and Leshem Choshen. 2026. Robustness as an Emergent Property of Task Performance. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 2847–2869, Budapest, Hungary. Association for Computational Linguistics.