Revealing the Inherent Instructability of Pre-Trained Language Models

• URL: http://arxiv.org/abs/2410.02465v2
• Date: Sun, 16 Feb 2025 13:50:42 GMT
• Title: Revealing the Inherent Instructability of Pre-Trained Language Models
• Authors: Seokhyun An, Minji Kim, Hyounghun Kim,
• Abstract summary: We show that Response Tuning (RT) removes the instruction and its corresponding mapping to the response from instruction tuning. Our experiments demonstrate that RT, trained only on responses, can effectively respond to a wide range of instructions and exhibit helpfulness approaching that of their instruction-tuned counterparts.
• Score: 9.504992236994697
• License:
• Abstract: Instruction tuning -- supervised fine-tuning using instruction-response pairs -- is a key step in making pre-trained large language models (LLMs) instructable. Meanwhile, LLMs perform multitask learning during their pre-training, acquiring extensive knowledge and capabilities. We hypothesize that the pre-training stage can enable them to develop the ability to comprehend and address instructions. To verify this, we propose Response Tuning (RT), which removes the instruction and its corresponding mapping to the response from instruction tuning. Instead, it focuses solely on establishing the response distribution. Our experiments demonstrate that RT models, trained only on responses, can effectively respond to a wide range of instructions and exhibit helpfulness approaching that of their instruction-tuned counterparts. In addition, we observe that the models can recognize and reject unsafe queries after learning the refusal conditions from training responses. Furthermore, we demonstrate that these observations also hold in an in-context learning setting. These findings support our hypothesis, highlighting the extensive inherent capabilities of pre-trained LLMs.

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