Superfluous Instruction: Vulnerabilities Stemming from Task-Specific Superficial Expressions in Instruction Templates (2025)

Abstract: Large language models (LLMs) achieve high performance through instruction-tuning, which involves learning various tasks using instruction templates. However, these templates often contain task-specific expressions, which are words that frequently appear in certain contexts but do not always convey the actual meaning of that context, even if they seem closely related to the target task. Biases inherent in such instruction templates may be learned by LLMs during training, potentially degrading performance when the models encounter superficial expressions. In this study, we propose a method that incorporates additional instructions to FLAN templates, without altering the base instruction to produce “superfluous instructions”. This allows us to investigate the vulnerabilities of LLMs caused by overfitting to task-specific expressions embedded in instruction templates. The experimental results revealed that the inclusion of superficial words strongly related to each task in the instruction text can alter the output, regardless of the intended meaning.

## introduction large language models (llms) adopt a training method called instruction-tuning @xcite , which enables them to respond appropriately to a wide range of user queries based on given instructions. to perform instructiontuning, it is necessary to construct datasets consisting of instruction-output pairs. instruction templates are typically designed to structure existing natural language processing tasks so that generative llms can produce relevant outputs. furthermore, diverse templates for each task are crucial to avoid overfitting to any single template. providing multiple templates during instruction-tuning is important for improving the model's generalization @xcite . however, templates designed for specific tasks often contain task-specific words, which may introduce biases related to those tasks. in this study, we focus on surface-level biases arising from the presence of task-specific words in instruction templates. by leveraging flan, a widely adopted instruction template dataset that allows for precise control over word occurrences, we can rigorously evaluate the influence of such taskspecific words. furthermore, we propose "superfluous instructions" which incorporate unrelated text into flan templates, while preserving the original task-solving intent of the instructions. for example, we add expressions such as "answer the following question without generating unrelated text". these expressions are carefully designed not to interfere with the original intent. therefore, we expect that they will not affect the model's output from a task-solving perspective. we evaluated three models tuned by flan instructions using 80 superfluous instructions tailored to each task. the results show that adding superfluous instructions, particularly those containing task-specific superficial expressions, negatively impacted performance. this suggests that instructiontuned llms are vulnerable to superficial cues in the instructions, which degrade performance even when the instruction's meaning remains unchanged. these findings provide important insights for developing more robust instruction-tuning methods. ## superfluous instructions we introduce "superfluous instructions" that contain target words for analysis but provide no new semantic information. by adding superficial expressions without semantic changes, we investigate how superficial expressions, such as task-specific words, affect model output. we use flan @xcite foot_0 as seed instruction templates. ## experimental results ## conclusion in this study, we proposed a novel method for designing instruction templates to analyze the impact of task-specific superficial expressions found in instruction-tuning templates on the performance of large language models. using this method, we generated instructions based on the flan templates and conducted evaluations on both mmlu and bbh tasks. the results revealed that the performance of llms is affected by task-specific superficial expressions included in the instructions. this insight is essential for developing more robust instruction-tuning methods. in future work, we plan to explore solutions such as replacing these superficial expressions during instruction-tuning to address the issues identified in this study.