Can Input Attributions Interpret the Inductive Reasoning Process in In-Context Learning?

• URL: http://arxiv.org/abs/2412.15628v3
• Date: Tue, 18 Feb 2025 06:17:50 GMT
• Title: Can Input Attributions Interpret the Inductive Reasoning Process in In-Context Learning?
• Authors: Mengyu Ye, Tatsuki Kuribayashi, Goro Kobayashi, Jun Suzuki,
• Abstract summary: In this paper, we design synthetic diagnostic tasks of inductive reasoning, inspired by the generalization tests in linguistics.<n>The question is whether conventional input attribution (IA) methods can track such a reasoning process, identify the influential example, in in ICL.
• Score: 19.268703560816498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interpreting the internal process of neural models has long been a challenge. This challenge remains relevant in the era of large language models (LLMs) and in-context learning (ICL); for example, ICL poses a new issue of interpreting which example in the few-shot examples contributed to identifying/solving the task. To this end, in this paper, we design synthetic diagnostic tasks of inductive reasoning, inspired by the generalization tests in linguistics; here, most in-context examples are ambiguous w.r.t. their underlying rule, and one critical example disambiguates the task demonstrated. The question is whether conventional input attribution (IA) methods can track such a reasoning process, i.e., identify the influential example, in ICL. Our experiments provide several practical findings; for example, a certain simple IA method works the best, and the larger the model, the generally harder it is to interpret the ICL with gradient-based IA methods.

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