PAFT: Prompt-Agnostic Fine-Tuning
While Large Language Models (LLMs) adapt well to downstream tasks after
fine-tuning, this adaptability often compromises prompt robustness, as even
minor prompt variations can significantly degrade performance. To address this,
we propose Prompt-Agnostic Fine-Tuning(PAFT), a simple yet effective approach
that dynamically adjusts prompts during fine-tuning. This encourages the model
to learn underlying task principles rather than overfitting to specific prompt
formulations. PAFT operates in two stages: First, a diverse set of meaningful,
synthetic candidate prompts is constructed. Second, during fine-tuning, prompts
are randomly sampled from this set to create dynamic training inputs. Extensive
experiments across diverse datasets and LLMs demonstrate that models trained
with PAFT exhibit strong robustness and generalization across a wide range of
prompts, including unseen ones. This enhanced robustness improves both model
performance and inference speed while maintaining training efficiency. Ablation
studies further confirm the effectiveness of PAFT.
67b576aa489d68b981e086ad
Chenxing Wei
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2025-02-19T10:23:00.016Z
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Chenxing Wei
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Yao Shu
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2025-02-19T14:37:34.696Z
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Shu Yao
user
ZCODE0
67b576aa489d68b981e086af
Mingwen Ou
67b576aa489d68b981e086b0
Ying Tiffany He
67b576aa489d68b981e086b1
Fei Richard Yu
PAFT: Prompt-Agnostic Fine-Tuning
While Large Language Models (LLMs) adapt well to downstream tasks after
fine-tuning, this adaptability often compromises prompt robustness, as even
minor prompt variations can significantly degrade performance. To address this,
we propose Prompt-Agnostic Fine-Tuning(PAFT), a simple yet effective approach
that dynamically adjusts prompts during fine-tuning. This encourages the model
to learn underlying task principles rather than overfitting to specific prompt
formulations. PAFT operates in two stages: First, a diverse set of meaningful,
synthetic candidate prompts is constructed. Second, during fine-tuning, prompts
are randomly sampled from this set to create dynamic training inputs. Extensive
experiments across diverse datasets and LLMs demonstrate that models trained
with PAFT exhibit strong robustness and generalization across a wide range of
prompts, including unseen ones. This enhanced robustness improves both model
performance and inference speed while maintaining training efficiency. Ablation
studies further confirm the effectiveness of PAFT.
Prompt-Agnostic Fine-Tuning (PAFT) dynamically adjusts prompts during training to improve LLM robustness and generalization.
Large Language Models (LLMs) fine-tuning prompt robustness Prompt-Agnostic Fine-Tuning (PAFT) synthetic prompt dynamic training inputs