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    <abstract>Code completion models often fail when the developer’s intent is under-specified in the code context. To mitigate this, developers frequently use natural language comments to clarify objectives. However, current code completion models fail to prioritize these directives effectively since they are merely pre-trained using the Fill-In-the-Middle (FIM) objective. On the one hand, the natural language instructions, mixed with the noisy code comments, are just treated as part of the background context within the prefix. On the other hand, the pre-training datasets for the FIM objective are mostly sourced from open-source repositories, which results in a scarcity of high-intent instruction-to-code pairings that reflect the developers’ workflow in code completion. To bridge this gap, we propose Instruction-aware Fill-In-the-Middle (IFIM), a fine-tuning method that extends the FIM structure with a dedicated, structurally separated instruction section. Our evaluation shows that IFIM substantially improves adherence to developer intent, while leaving infilling performance unchanged when no instruction is given. The gains hold on an in-the-wild benchmark of 100 instructions written by real developers and across model scales from 1.5B to 7B. IFIM thus offers a backward-compatible upgrade path for existing FIM-based code completion systems at a modest training cost.</abstract>
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%0 Conference Proceedings
%T Extending Fill-In-the-Middle with Instructions for Steerable Code Completion
%A Sun, Zhensu
%A Yang, Chengran
%A Peng, Chao
%A Gao, Pengfei
%A Du, Xiaoning
%A Li, Li
%A Lo, David
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F sun-etal-2026-extending
%X Code completion models often fail when the developer’s intent is under-specified in the code context. To mitigate this, developers frequently use natural language comments to clarify objectives. However, current code completion models fail to prioritize these directives effectively since they are merely pre-trained using the Fill-In-the-Middle (FIM) objective. On the one hand, the natural language instructions, mixed with the noisy code comments, are just treated as part of the background context within the prefix. On the other hand, the pre-training datasets for the FIM objective are mostly sourced from open-source repositories, which results in a scarcity of high-intent instruction-to-code pairings that reflect the developers’ workflow in code completion. To bridge this gap, we propose Instruction-aware Fill-In-the-Middle (IFIM), a fine-tuning method that extends the FIM structure with a dedicated, structurally separated instruction section. Our evaluation shows that IFIM substantially improves adherence to developer intent, while leaving infilling performance unchanged when no instruction is given. The gains hold on an in-the-wild benchmark of 100 instructions written by real developers and across model scales from 1.5B to 7B. IFIM thus offers a backward-compatible upgrade path for existing FIM-based code completion systems at a modest training cost.
%U https://aclanthology.org/2026.findings-emnlp.191/
%P 4050-4066

Markdown (Informal)

[Extending Fill-In-the-Middle with Instructions for Steerable Code Completion](https://aclanthology.org/2026.findings-emnlp.191/) (Sun et al., Findings 2026)

• Extending Fill-In-the-Middle with Instructions for Steerable Code Completion (Sun et al., Findings 2026)

ACL

• Zhensu Sun, Chengran Yang, Chao Peng, Pengfei Gao, Xiaoning Du, Li Li, and David Lo. 2026. Extending Fill-In-the-Middle with Instructions for Steerable Code Completion. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 4050–4066, Budapest, Hungary. Association for Computational Linguistics.