[2410.03140] In-context Learning in Presence of Spurious Correlations

Computer Science > Machine Learning arXiv:2410.03140 (cs) [Submitted on 4 Oct 2024 (v1), last revised 1 Apr 2026 (this version, v2)] Title:In-context Learning in Presence of Spurious Correlations Authors:Hrayr Harutyunyan, Rafayel Darbinyan, Samvel Karapetyan, Hrant Khachatrian View a PDF of the paper titled In-context Learning in Presence of Spurious Correlations, by Hrayr Harutyunyan and 3 other authors View PDF HTML (experimental) Abstract:Large language models exhibit a remarkable capacity for in-context learning, where they learn to solve tasks given a few examples. Recent work has shown that transformers can be trained to perform simple regression tasks in-context. This work explores the possibility of training an in-context learner for classification tasks involving spurious features. We find that the conventional approach of training in-context learners is susceptible to spurious features. Moreover, when the meta-training dataset includes instances of only one task, the conventional approach leads to task memorization and fails to produce a model that leverages context for predictions. Based on these observations, we propose a novel technique to train such a learner for a given classification task. Remarkably, this in-context learner matches and sometimes outperforms strong methods like ERM and GroupDRO. However, unlike these algorithms, it does not generalize well to other tasks. We show that it is possible to obtain an in-context learner that generalizes to unsee...