@inproceedings{li-etal-2026-interpretation,
title = "Can Interpretation Predict Behavior on Unseen Data?",
author = "Li, Victoria R and
Kaufmann, Jenny and
Qin, Tian and
Wattenberg, Martin and
Alvarez-Melis, David and
Saphra, Naomi",
editor = "Arad, Dana and
Belinkov, Yonatan and
Chen, Hanjie and
Feldhus, Nils and
Mueller, Aaron and
Mohebbi, Hosein and
Sarti, Gabriele and
Tutek, Martin",
booktitle = "Proceedings of the 9th {B}lackbox{NLP} Workshop: Analyzing and Interpreting Neural Networks for {NLP}",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.blackboxnlp-1.24/",
pages = "384--407",
ISBN = "979-8-89176-476-7",
abstract = "Interpretability research often predicts model responses to targeted mechanistic $\textit{interventions}$. But can we predict responses to unseen $\textit{input data}$? We propose and demonstrate this alternate objective by using model internals to predict their out-of-distribution (OOD) behavior. We train hundreds of Transformers on simple synthetic tasks, where perfect in-distribution accuracy is compatible with multiple OOD generalization rules. We successfully use attention patterns{---}observed only on in-distribution data{---}to predict which rule each model follows on OOD data. Our experiments decouple the mechanistic faithfulness of our interpretation from its predictive value; ablations reveal such internal patterns can $\textit{suppress}$ rather than $\textit{support}$ the rule they predict, showing observational analysis can forecast behavior even when causal analysis fails to support a simple cause-effect link. Our findings are a proof-of-concept for a new interpretability objective: understanding model internals to predict behavior and assess reliability under distribution shift."
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<abstract>Interpretability research often predicts model responses to targeted mechanistic interventions. But can we predict responses to unseen input data? We propose and demonstrate this alternate objective by using model internals to predict their out-of-distribution (OOD) behavior. We train hundreds of Transformers on simple synthetic tasks, where perfect in-distribution accuracy is compatible with multiple OOD generalization rules. We successfully use attention patterns—observed only on in-distribution data—to predict which rule each model follows on OOD data. Our experiments decouple the mechanistic faithfulness of our interpretation from its predictive value; ablations reveal such internal patterns can suppress rather than support the rule they predict, showing observational analysis can forecast behavior even when causal analysis fails to support a simple cause-effect link. Our findings are a proof-of-concept for a new interpretability objective: understanding model internals to predict behavior and assess reliability under distribution shift.</abstract>
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%0 Conference Proceedings
%T Can Interpretation Predict Behavior on Unseen Data?
%A Li, Victoria R.
%A Kaufmann, Jenny
%A Qin, Tian
%A Wattenberg, Martin
%A Alvarez-Melis, David
%A Saphra, Naomi
%Y Arad, Dana
%Y Belinkov, Yonatan
%Y Chen, Hanjie
%Y Feldhus, Nils
%Y Mueller, Aaron
%Y Mohebbi, Hosein
%Y Sarti, Gabriele
%Y Tutek, Martin
%S Proceedings of the 9th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-476-7
%F li-etal-2026-interpretation
%X Interpretability research often predicts model responses to targeted mechanistic interventions. But can we predict responses to unseen input data? We propose and demonstrate this alternate objective by using model internals to predict their out-of-distribution (OOD) behavior. We train hundreds of Transformers on simple synthetic tasks, where perfect in-distribution accuracy is compatible with multiple OOD generalization rules. We successfully use attention patterns—observed only on in-distribution data—to predict which rule each model follows on OOD data. Our experiments decouple the mechanistic faithfulness of our interpretation from its predictive value; ablations reveal such internal patterns can suppress rather than support the rule they predict, showing observational analysis can forecast behavior even when causal analysis fails to support a simple cause-effect link. Our findings are a proof-of-concept for a new interpretability objective: understanding model internals to predict behavior and assess reliability under distribution shift.
%U https://aclanthology.org/2026.blackboxnlp-1.24/
%P 384-407
Markdown (Informal)
[Can Interpretation Predict Behavior on Unseen Data?](https://aclanthology.org/2026.blackboxnlp-1.24/) (Li et al., BlackboxNLP 2026)
ACL
- Victoria R Li, Jenny Kaufmann, Tian Qin, Martin Wattenberg, David Alvarez-Melis, and Naomi Saphra. 2026. Can Interpretation Predict Behavior on Unseen Data?. In Proceedings of the 9th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pages 384–407, Budapest, Hungary. Association for Computational Linguistics.