The Missing Alignment Link of In-context Learning on Sequences

Large language models (LLMs) have demonstrated the capability to perform in-context learning (ICL) for completely unseen tasks in classification or language completion. Sequence to sequence (Seq2Seq) is another popular task category with several applications seeking quick adaptation with ICL. We present a systematic analysis of the ICL capability of LLMs on Seq2Seq tasks using a formal structured language-pair. Our study reveals a critical limitation: except for very short input sequences, ICL fails to achieve consistent learning across all output positions. This exposes a fundamental weakness of modern LLMs -their inability to effectively uncover the alignment between input and output sequences. Consequently, this limitation results in incomplete induction heads, which form the basis for in-context learning of new discrete mappings. To address these limitations, we propose ICA-Tune, a method for focused fine-tuning of an LLM using in-context examples. We present a mechanistic evaluation with two accuracy probes to show how alignment emerges in middle layers of an LLM without any direct supervision. This alignment leads to an abrupt jump in the completeness of the induction heads in higher layers. We show that compared to standard fine-tuning, ICA-Tune enables more sample efficient learning and generalizes better to OOD instances.