LARFT: Closing the Cognition-Action Gap for Length Instruction Following in Large Language Models

1. 1Identify the limitation of existing methods in controlling output length
2. 2Recognize the importance of addressing the model's intrinsic deficit in length cognition
3. 3Implement LARFT to enforce length constraints internally
4. 4Evaluate the performance of LARFT on complex instruction-following tasks

AI engineers and ML researchers benefit from LARFT as it improves the precision of output length control in large language models, enabling more effective instruction-following tasks

💡 Internal length cognition is crucial for precise output length control in large language models

🤖 LARFT closes the cognition-action gap for length instruction following in LLMs