Mitigating Copy Bias in In-Context Learning through Neuron Pruning (2026.findings-eacl)
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| Challenge: | Large language models have shown impressive few-shot in-context learning abilities, but they are prone to a ‘copying bias’, where they copy answers from provided examples instead of learning the underlying patterns. |
| Approach: | They propose a method to prune neurons that prioritize copying over generalization and adopt a task-recognition perspective on ICL and examine task vectors induced by the model. |
| Outcome: | The proposed method improves performance across a diverse set of ICL tasks while maintaining or improving the model’s general capabilities. |
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