London Lowmanstone

John Harwell, London Lowmanstone, Maria Gini

Modern intelligent systems researchers employ the scientific method: they form hypotheses about system behavior, and then run experiments using one or more independent variables to test their hypotheses. We present SIERRA, a novel framework structured around that idea for accelerating research developments and improving reproducibility of results. SIERRA makes it easy to quickly specify the independent variable(s) for an experiment, generate experimental inputs, automatically run the experiment, and process the results to generate deliverables such as graphs and videos. SIERRA provides reproducible automation independent of the execution environment (HPC hardware, real robots, etc.) and targeted platform (arbitrary simulator or real robots), enabling exact experiment replication (up to the limit of the execution environment and platform). It employs a deeply modular approach that allows easy customization and extension of automation for the needs of individual researchers, thereby eliminating manual experiment configuration and result processing via throw-away scripts.

London Lowmanstone, Ruyuan Wan, Risako Owan et al.

Annotating data via crowdsourcing is time-consuming and expensive. Due to these costs, dataset creators often have each annotator label only a small subset of the data. This leads to sparse datasets with examples that are marked by few annotators. The downside of this process is that if an annotator doesn't get to label a particular example, their perspective on it is missed. This is especially concerning for subjective NLP datasets where there is no single correct label: people may have different valid opinions. Thus, we propose using imputation methods to generate the opinions of all annotators for all examples, creating a dataset that does not leave out any annotator's view. We then train and prompt models, using data from the imputed dataset, to make predictions about the distribution of responses and individual annotations. In our analysis of the results, we found that the choice of imputation method significantly impacts soft label changes and distribution. While the imputation introduces noise in the prediction of the original dataset, it has shown potential in enhancing shots for prompts, particularly for low-response-rate annotators. We have made all of our code and data publicly available.