Uncovering Hidden Consequences of Pre-training Objectives in Sequence-to-Sequence Models (2023)

Abstract: Some variants of self-supervised denoising objectives for pre-training encoder-decoder language models have been reported to have a negligible impact on downstream performance. Yet the design of these pre-training objectives leads to behavioural differences that can be uncovered with specific manipulations. We reproduce a recently proposed zero-shot control method and find that it is only successful on a subset of models. To understand what causes the difference in its effectiveness, we perform a set of controlled experiments, varying only the pre-training objective, and find unexpected interactions between the pre-training method and downstream controllability of models after fine-tuning. Our results show that different pre-training objectives have consequences that may not be visible in standard downstream evaluation, but which should be taken into account when developing models with controllability in mind.

## introduction self-supervised denoising objectives have proven extremely powerful for deriving transformer-based pre-trained language models (plms) given massive amounts of unlabelled data. these objectives are typically agnostic towards specific downstream tasks and thus do not resemble real-world use cases. instead, they enable the model to learn optimal parameter initialisations for subsequent fine-tuning on various downstream tasks @xcite . during fine-tuning, the plm quickly learns new tasks based on the supervised signal provided, rendering pre-training task largely redundant. previous work has found performance differences on downstream tasks to be negligible given various denoising pre-training objectives @xcite . 1 as figure 1 : the effect of ctxaug for inquisitive dialogue modelling with off-the-shelf models. in contrast to bart, t5 models exhibit a minimal response to the context code. t5-small-lm refers to the lm-adapted model from @xcite . a result, the choice of which method to apply in pre-training has largely been based on factors such as efficiency (e.g. @xcite . however, given equally well performing pre-training objectives, we find that encoderdecoder plms respond drastically differently to post-hoc manipulations after fine-tuning. specifically, we investigate the use of context augmentation (ctxaug), proposed by @xcite , as a zero-shot control method designed to steer a fine-tuned encoder-decoder model towards generating outputs with particular attributes. while they introduce this as a general control mechanism for encoder-decoder transformers, our experiments with bart @xcite and two variants of t5 @xcite show that controllability via context augmentation is predominantly exhibited by bart (figure 1 ). given this observation, we hypothesise that the success of this zero-shot control method may be highly dependent on a model's pre-training objective. to investigate this hypothesis, we set out to identify exactly what aspects of bart's pretraining allow for ctxaug to work. our findings suggest that fine-tuned models are capable of exhibiting vestigial behaviours foot_1 which are endowed by their pre-training objectives and allow for interesting and useful post-hoc manipulation methods in downstream applications. ## related work the study of plms, their abilities, properties and behaviours, occupies a significant space in today's nlp research (e.g. @xcite @xcite . numerous works have evaluated and compared downstream performance of seq2seq plms, covering a wide array of tasks including abstractive summarisation @xcite @xcite , question answering @xcite , graph-to-text generation @xcite , dialogue modelling @xcite ) and text simplification @xcite , among others. while such comparisons are useful for guiding researchers in selecting the right model for a task and can sometimes reveal interesting differences on certain task-specific data sets, they tend to neglect important differences between plms, such as the underlying model size or the type and amount of data used for pre-training. thus, it remains difficult to explain exactly why a particular model performs better or worse on a given task. meanwhile, there is a growing body of literature aimed at explaining some of the interesting and often unexpected behaviours observed among large plms. in this area, multilinguality has been linked to the duration of fine-tuning @xcite , and the ability to perform in-context fewshot learning and zero-shot generalisation has been linked to multiple factors. these include model scale @xcite , the types and formatting of demonstrations @xcite , memorisation of pre-training data @xcite and its distributional properties @xcite . the selection of architecture and pre-training objectives have also been found to be influential @xcite . our work falls into this category and aims to explain which aspects of seq2seq pre-training objectives contribute to the ability to exploit additional conditioning context provided at inference time. ## conclusions as plms become increasingly commonplace, so too does the importance of understanding the potential downstream consequences of decisions relating to their design. our experiments indicate that context augmentation, as a method for zero-shot controlled natural language generation, is susceptible to inductive biases learned in pre-training given different types of control codes. based on this, we conclude that pre-training objectives that aim to reconstruct a noised input in full, similar to bart, are best suited to leverage this technique. looking forward, we expect that even for seemingly equally effective pre-training objectives, we can identify differences in behaviour, e.g. applicability of control methods, that remain after fine-tuning. in searching for optimal pre-training strategies for plms, this opens another dimension that needs to be considered and better understood.