Methods for Estimating and Improving Robustness of Language Models (2022)

Abstract: Despite their outstanding performance, large language models (LLMs) suffer notorious flaws related to their preference for shallow textual relations over full semantic complexity of the problem. This proposal investigates a common denominator of this problem in their weak ability to generalise outside of the training domain. We survey diverse research directions providing estimations of model generalisation ability and find that incorporating some of these measures in the training objectives leads to enhanced distributional robustness of neural models. Based on these findings, we present future research directions enhancing the robustness of LLMs.

## introduction the advances in language processing that we observe in recent years, mostly led by the instances of large language models (llms) based on the transformer architecture @xcite raise a deserved attention of the scientific community. we find studies concluding that llms fine-tuned for a specific task can align with, or even outperform human accuracy on complex tasks such as question answering @xcite , paraphrase identification @xcite , machine translation @xcite and others. in contrast, critical studies demonstrate that many of the models reaching a state-of-the-art on a given task perform poorly on data sets drawn from different distribution(s). this is due to various reasons, such as training data set biases including spurious linguistic correlations @xcite , different text stylistics or typos @xcite , where a broad preference of llms towards fitting non-representative, yet easy-to-learn surface-level relations cause them to under-perform even shallow networks @xcite . a lack of generalisation can also be caused by procedural reasons, such as training process instability, causing a convergence to local minima of distinct generalisation quality @xcite . low robustness of the consequential model towards out-of-distribution (ood) samples limits their practical usability to the samples drawn from the training distribution, which is often impossible to ensure. despite that the complex language models strike an impression of a black-box, an extensive branch of research demonstrated that internal representations of llms correspond well to a human taxonomy in terms of morphological and syntactic decomposition @xcite , or that the depth of the internal representation correlates well with the complexity of the problem as perceived by humans @xcite . the reported agility support the central presumption of this proposal; that llms can avoid the problems mentioned above under additional regularisation. we argue that such regularisation could also strenghten the implicit property of llms learning compositional language features and thus enhance an interpretability of their decision-making. in this proposal, we survey literature from the broader area of neural networks for the reasons for better generalisation of the neural model. we find that many measures reported to correlate well with model's ood performance can also enhance neural model generalisation when utilised within the model's training objective, as regularisers, or additional components of the training cost function. inspired by this finding, this proposal outlines a path towards identification and utilisation of generalisation measures aimed to enhance robustness of llms towards distribution shift. rq1: "can we estimate the performance of llms on data from ood, without a collection of annotated data or expert feedback?" rq2: "can we adjust the process of training llms to perform better on ood samples?" in section 2.1 we survey the studies aiming to estimate robustness of neural models with no restrictions on a domain of application. subsequently, in section 2.2, we survey the training techniques reported to enhance the robustness of the trained model. based on these findings, in section 3 we identify promising directions and respective challenges specific for estimating ( §3.1) and enhancing ( §3.2) the robustness of llms. ## research proposal following the referenced studies on evaluation and enhancement of the generalisation of neural models, this section outlines directions in measuring and improving robustness of llms, respectively. ## conclusion our work outlines potential directions in enhancing distributional robustness of llms to mitigate a performance drop under distribution shift. we survey and identify accurate generalisation measures ( §2.1) and find multiple studies demonstrating that utilisation of these measures in the training objectives positively impacts model robustness ( §2.2). following this observation, we propose to identify generalisation measures best-suitable for llms ( §3.1) and outline ways how to utilise these measures in the training process. additionally, we identify a set of other methods reported to enhance ood performance of llms that we propose to compare to in the outlined methodology for evaluating generalisation measures. similarly, we propose methodologies for robustness estimation of both generative and discriminative llms ( §3.2); these methodologies are based on a quality assessment on the domains covered by the enclosed set of variables, and on the robustness towards the data set(s) constructed to expose enclosed set of models' biases.