We compare diffusion-based and autoregressive language models under CheckList-style perturbations. We frame semantics-preserving perturbations as potentially containing an off-manifold component and hypothesise that diffusion denoising can reduce the resulting output change relative to autoregressive decoding. We evaluate eight instruction-tuned 7-9B models (three diffusion, five autoregressive) across sentiment, QQP, and SQuAD using behavioural pass rates and a perturbation coefficient that measures output change relative to input change. Results are task-dependent. Behaviourally, diffusion gains 18.2 and 11.0 points overall on QQP and SQuAD, while autoregressive models perform better on the sentiment and SQuAD invariance slices. On QQP, the all-model coefficient ratio does not favour diffusion ( ̂γ_AR/ ̂α_Diff ≈ 0.91); excluding two autoregressive models with strong label-prior effects as a diagnostic sensitivity analysis yields 1.23 overall and 1.48 on invariance tests. Diffusion also performs worse on perturbations that should change the output. Thus, the observed stability differences are task-conditional, and because the empirical diffusion coefficients exceed one, the experiments do not directly validate the contraction assumption of our theoretical proposition. Data and code are available at: https://github.com/AarushRathore1729/dlm_ar_final.

We present our approaches and methods for SemEval-2021 Task-4 Reading Comprehension of Abstract Meaning. Given a question with a fill-in-the-blank, and a corresponding context, the task is to predict the most suitable word from a list of 5 options. There are three subtasks: Imperceptibility, Non-Specificity and Intersection. We use encoders of transformers-based models pretrained on the MLM task to build our Fill-in-the-blank (FitB) models. Moreover, to model imperceptibility, we define certain linguistic features, and to model non-specificity, we leverage information from hypernyms and hyponyms provided by a lexical database. Specifically, for non-specificity, we try out augmentation techniques, and other statistical techniques. We also propose variants, namely Chunk Voting and Max Context, to take care of input length restrictions for BERT, etc. Additionally, we perform a thorough ablation study, and use Integrated Gradients to explain our predictions on a few samples. Our models achieve accuracies of 75.31% and 77.84%, on the test sets for subtask-I and subtask-II, respectively. For subtask-III, we achieve accuracies of 65.64% and 64.27%.