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Findings
- IC-1015GPT-J and 10 other LLMs exhibit overthinking: calibrated accuracy given incorrect few-shot demonstrations peaks at a critical layer then declines, and ablating 5 false induction heads in late layers reduces the accuracy gap by 38.9% on average [eval]
- IC-1088ICL predictions in LLaMA, LLaMA-2, and Falcon models depend on in-context label information and can learn truly novel label relationships [eval]
- IC-1089ICL in LLaMA, LLaMA-2, and Falcon models cannot fully overcome pre-training label preferences when in-context labels are flipped [eval]
- IC-1090ICL in LLaMA, LLaMA-2, and Falcon models preferentially uses in-context label information closer to the query rather than treating all examples equally [eval]
- IC-575Four released LLMs (LLaMA-3.1-8B, Mistral-7B, Qwen2-7B, Yi-1.5-9B) can perform in-context learning on continuous vector representations projected into their embedding space, matching or outperforming few-shot ICL across text, time-series, graph, and fMRI tasks [eval]
- IC-577An encoder's text reconstruction performance positively correlates with its effectiveness in downstream vector-ICL classification tasks across 15 encoder-LLM-dataset configurations [eval]
- IC-578LLMs encode input text as linearly separable representations in forerunner token hidden states, emerging in early layers and enhanced by in-context demonstrations [eval]
- IC-580The 3-step ICL inference circuit (text encoding, semantics merge, feature retrieval) is a dominant causal mechanism, as ablating the corresponding attention connections significantly degrades ICL accuracy [eval]
- IC-581Induction heads for ICL operate on task-specific attention subspaces, with partial overlap across tasks, and the geometry of these subspaces explains demonstration saturation [eval]