Exploring Instruction Tuning Limitations

The paper A Closer Look at the Limitations of Instruction Tuning critically examines the effects of Instruction Tuning (IT) on Large Language Models (LLMs). The authors use rigorous experiments to uncover IT’s limitations, arguing that IT does not enhance knowledge or skills in LLMs, and can degrade the quality of responses or even increase hallucinations.

• IT is inadequate for knowledge enhancement in LLMs.
• LoRA fine-tuning is limited and full-parameter fine-tuning diminishes knowledge.
• IT-derived copying patterns decrease response quality.
• Popular IT improvement methods fail to outperform simple LoRA fine-tuning.
• Pre-trained knowledge generates better responses than learned knowledge from IT.

IT raises important questions about the effectiveness of current methods in improving LLMs. It prompts a re-evaluation of strategies to enhance the functionalities of these models without compromising their pre-trained knowledge base.