Function Induction and Task Generalization: An Interpretability Study with Off-by-One Addition

• URL: http://arxiv.org/abs/2507.09875v1
• Date: Mon, 14 Jul 2025 03:20:55 GMT
• Title: Function Induction and Task Generalization: An Interpretability Study with Off-by-One Addition
• Authors: Qinyuan Ye, Robin Jia, Xiang Ren,
• Abstract summary: We find a function induction mechanism that explains the model's generalization from standard addition to off-by-one addition.<n>This mechanism resembles the structure of the induction head mechanism found in prior work and elevates it to a higher level of abstraction.<n>We find that this function induction mechanism is reused in a broader range of tasks, including synthetic tasks such as shifted multiple-choice QA and algorithmic tasks such as base-8 addition.
• Score: 52.11481619456093
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models demonstrate the intriguing ability to perform unseen tasks via in-context learning. However, it remains unclear what mechanisms inside the model drive such task-level generalization. In this work, we approach this question through the lens of off-by-one addition (i.e., 1+1=3, 2+2=5, 3+3=?), a two-step, counterfactual task with an unexpected +1 function as a second step. Leveraging circuit-style interpretability techniques such as path patching, we analyze the models' internal computations behind their notable performance and present three key findings. First, we uncover a function induction mechanism that explains the model's generalization from standard addition to off-by-one addition. This mechanism resembles the structure of the induction head mechanism found in prior work and elevates it to a higher level of abstraction. Second, we show that the induction of the +1 function is governed by multiple attention heads in parallel, each of which emits a distinct piece of the +1 function. Finally, we find that this function induction mechanism is reused in a broader range of tasks, including synthetic tasks such as shifted multiple-choice QA and algorithmic tasks such as base-8 addition. Overall, our findings offer deeper insights into how reusable and composable structures within language models enable task-level generalization.

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