Can Input Attributions Explain Inductive Reasoning in In-Context Learning?
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911046217760768 |
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| author | Ye, Mengyu Kuribayashi, Tatsuki Kobayashi, Goro Suzuki, Jun |
| author_facet | Ye, Mengyu Kuribayashi, Tatsuki Kobayashi, Goro Suzuki, Jun |
| contents | 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 typically adopted in psycholinguistics. Here, most in-context examples are ambiguous w.r.t. their underlying rule, and one critical example disambiguates it. 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_15628 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can Input Attributions Explain Inductive Reasoning in In-Context Learning? Ye, Mengyu Kuribayashi, Tatsuki Kobayashi, Goro Suzuki, Jun Computation and Language 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 typically adopted in psycholinguistics. Here, most in-context examples are ambiguous w.r.t. their underlying rule, and one critical example disambiguates it. 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. |
| title | Can Input Attributions Explain Inductive Reasoning in In-Context Learning? |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.15628 |