Feeding a Small Transformer's Summary Into the Prompt Lifts LLM Code Summarisation BLEU-4 by 7.8%

arXiv 2607.20933·low signal

The paper diagnoses a measurement problem — task-specific Transformer summaries score well on NLG metrics that reward lexical overlap, while abstractive LLM summaries are semantically better but score poorly because they use different words than developer-written references — and proposes combining the two rather than choosing. Prompting four LLMs with four prompt variants, each assisted by a task-specific Transformer's output embedded in the prompt, yields a 7.8% BLEU-4 improvement and 5% on a second metric. The framing targets the Secure SDLC maintenance phase, where missing, incomplete, or outdated summaries drive bugs and vulnerabilities.