Large language models (LLMs) have recently shown great potential for in-context learning, where LLMs learn a new task simply by conditioning on a few input-label pairs (prompts). Despite their potential, our understanding of the factors influencing end-task performance and the robustness of in-context learning remains limited. This paper aims to bridge this knowledge gap by investigating the reliance of LLMs on shortcuts or spurious correlations within prompts. Through comprehensive experiments on classification and extraction tasks, we reveal that LLMs are “lazy learners” that tend to exploit such shortcuts. Additionally, we uncover a surprising finding that larger models are more likely to utilize shortcuts in prompts during inference. Our findings provide a new perspective on evaluating robustness in in-context learning and pose new challenges for detecting and mitigating the use of shortcuts in prompts.

Textual adversarial attacks can discover models’ weaknesses by adding semantic-preserved but misleading perturbations to the inputs. The long-lasting adversarial attack-and-defense arms race in Natural Language Processing (NLP) is algorithm-centric, providing valuable techniques for automatic robustness evaluation. However, the existing practice of robustness evaluation may exhibit issues of incomprehensive evaluation, impractical evaluation protocol, and invalid adversarial samples. In this paper, we aim to set up a unified automatic robustness evaluation framework, shifting towards model-centric evaluation to further exploit the advantages of adversarial attacks. To address the above challenges, we first determine robustness evaluation dimensions based on model capabilities and specify the reasonable algorithm to generate adversarial samples for each dimension. Then we establish the evaluation protocol, including evaluation settings and metrics, under realistic demands. Finally, we use the perturbation degree of adversarial samples to control the sample validity. We implement a toolkit RobTest that realizes our automatic robustness evaluation framework. In our experiments, we conduct a robustness evaluation of RoBERTa models to demonstrate the effectiveness of our evaluation framework, and further show the rationality of each component in the framework.

Automatic offensive language detection has become a crucial issue in recent years. Existing researches on this topic are usually based on a large amount of data annotated at sentence level to train a robust model. However, sentence-level annotations are expensive in practice as the scenario expands, while there exist a large amount of natural labels from historical information on online platforms such as reports and punishments. Notably, these natural labels are usually in bag-level corresponding to the whole documents (articles, user profiles, conversations, etc.). Therefore, we target at proposing an approach capable of utilizing the bag-level labeled data for offensive language detection in this study. For this purpose, we formalize this task into a multiple instance learning (MIL) problem. We break down the design of existing MIL methods and propose a hybrid fusion MIL model with mutual-attention mechanism. In order to verify the validity of the proposed method, we present two new bag-level labeled datasets for offensive language detection: OLID-bags and MINOR. Experimental results based on the proposed datasets demonstrate the effectiveness of the mutual-attention method at both sentence level and bag level.