Modern language models (LMs) need to follow human instructions while being faithful; yet, they often fail to achieve both.Here, we provide concrete evidence of a trade-off between instruction following (i.e., follow open-ended instructions) and faithfulness (i.e., ground responses in given context) when training LMs with these objectives.For instance, fine-tuning LLaMA-7B on instruction following datasets renders it less faithful.Conversely, instructiontuned Vicuna-7B shows degraded performance at following instructions when further optimized on tasks that require contextual grounding.One common remedy is multi-task learning (MTL) with data mixing, yet it remains far from achieving a synergic outcome.We propose a simple yet effective method that relies on Rejection Sampling for Continued Selfinstruction Tuning (RESET), which significantly outperforms vanilla MTL.Surprisingly, we find that less is more, as training RESET with high-quality, yet substantially smaller data (three-fold less) yields superior results.Our findings offer a better understanding of objective discrepancies in alignment training of LMs.

Yian Zhang, Alex Warstadt, Xiaocheng Li, Samuel R. Bowman. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

One reason pretraining on self-supervised linguistic tasks is effective is that it teaches models features that are helpful for language understanding.However, we want pretrained models to learn not only to represent linguistic features, but also to use those features preferentially during fine-turning.With this goal in mind, we introduce a new English-language diagnostic set called MSGS (the Mixed Signals Generalization Set), which consists of 20 ambiguous binary classification tasks that we use to test whether a pretrained model prefers linguistic or surface generalizations during finetuning.We pretrain RoBERTa models from scratch on quantities of data ranging from 1M to 1B words and compare their performance on MSGS to the publicly available RoBERTa BASE .We find that models can learn to represent linguistic features with little pretraining data, but require far more data to learn to prefer linguistic generalizations over surface ones.Eventually, with about 30B words of pretraining data, RoBERTa BASE does demonstrate a linguistic bias with some regularity.We conclude that while self-supervised pretraining is an effective way to learn helpful inductive biases, there is likely room to improve the rate at which models learn which features matter. Feature type Feature description Positive example Negative example SurfaceAbsolute position Is the first token of S "the"?The cat chased a mouse.A cat chased a mouse.Length Is S longer than n (e.g., 3) words?The cat chased a mouse.The cat meowed.Lexical content Does S contain "the"?That cat chased the mouse.That cat chased a mouse.Relative position Does "the" precede "a"?The cat chased a mouse.A cat chased the mouse.Orthography Does S appear in title case?The Cat Chased a Mouse.The cat chased a mouse. LinguisticMorphology Does S have an irregular past verb?The cats slept.The cats meow.Syn.category Does S have an adjective?Lincoln was tall.Lincoln was president.Syn.construction Is S the control construction?Sue is eager to sleep.Sue is likely to sleep.Syn.position Is the main verb in "ing" form?Cats who eat mice are purring.Cats who are eating mice purr.