Instruction tuning plays a crucial role in shaping the outputs of language
models (LMs) to desired styles. In this work, we propose a simple yet effective
method, Instruction Modelling (IM), which trains LMs by applying a loss
function to the instruction and prompt part rather than solely to the output
part. Through experiments across 21 diverse benchmarks, we show that, in many
scenarios, IM can effectively improve the LM performance on both NLP tasks
(e.g., MMLU, TruthfulQA, and HumanEval) and open-ended generation benchmarks
(e.g., MT-Bench and AlpacaEval). Remarkably, in the most advantageous case, IM
boosts model performance on AlpacaEval 1.0 by over 100%. We identify two key
factors influencing the effectiveness of IM: (1) The ratio between instruction
length and output length in the training data; and (2) The number of training
examples. We observe that IM is especially beneficial when trained on datasets
with lengthy instructions paired with brief outputs, or under the Superficial
Alignment Hypothesis (SAH) where a small amount of training examples are used
for instruction tuning. Further analysis substantiates our hypothesis that the
improvement can be attributed to reduced overfitting to instruction tuning
datasets. Our work provides practical guidance for instruction tuning LMs,
especially in low-resource scenarios.

通过实验证明，Instruction Modelling 能够提高语言模型的性能，特别是在指令调整数据集不充足、指令长度与输出长度不匹配以及减少过拟合情况下，对低资源场景中的语言模型进行指令调整提供了实用指南。