arXiv:2606.01967v1 Announce Type: new Abstract: While prompt engineering is instrumental in maximizing the capabilities of Large Language Models (LLMs) during inference, the role of prompts during training remains critically underexplored. Prevailing fine-tuning paradigms typically treat training prompts as mere surface forms, assuming that semantically equivalent instructions yield identical learning outcomes. However, we reveal that this equivalence is deceptive: while paraphrased prompts often lead to comparable in-task performance, they induce drastically different cross-task impacts regar
This research addresses a critical gap in understanding LLM training dynamics, specifically the subtle but significant impact of prompt wording on model generalization, as LLMs become more ubiquitous.
Sophisticated readers should care because this insight suggests that optimizing training prompts, not just inference prompts, is crucial for developing robust and versatile AI models, directly influencing their deployment and effectiveness across various applications.
The understanding that prompt phrasing during fine-tuning fundamentally alters an LLM's cross-task performance necessitates a more rigorous and state-adaptive approach to model development, moving beyond simple surface-form equivalence.
- · AI researchers and developers focused on fine-tuning
- · Companies with proprietary LLMs seeking generalizable models
- · AI platforms offering advanced fine-tuning tools
- · Organizations relying on simplistic fine-tuning methodologies
- · Generic prompt engineering services that don't consider training phase impacts
Fine-tuning methodologies for Large Language Models will incorporate more sophisticated prompt optimization techniques.
This could lead to LLMs that are more generalizable and require less task-specific fine-tuning post-deployment, reducing operational costs.
The distinction between pre-training and fine-tuning becomes increasingly blurred, as 'training prompt' design becomes its own specialized field, potentially impacting the skill sets required for AI development.
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Read at arXiv cs.CL