Large language model (LLM)-powered code review automation tools have been introduced to generate code review comments. However, not all generated comments will drive code changes. Understanding what types of generated review comments are likely to trigger code changes is crucial for identifying those that are actionable. In this paper, we set out to investigate (1) the types of review comments written by humans and LLMs, and (2) the types of generated comments that are most frequently resolved by developers. To do so, we developed an LLM-as-a-Judge to automatically classify review comments based on our own taxonomy of five categories. Our empirical study confirms that (1) the LLM reviewer and human reviewers exhibit distinct strengths and weaknesses depending on the project context, and (2) readability, bugs, and maintainability-related comments had higher resolution rates than those focused on code design. These results suggest that a substantial proportion of LLM-generated comments are actionable and can be resolved by developers. Our work highlights the complementarity between LLM and human reviewers and offers suggestions to improve the practical effectiveness of LLM-powered code review tools.

We present a novel task and dataset for evaluating the ability of vision and language models to conduct visio-linguistic compositional reasoning, which we call Winoground. Given two images and two captions, the goal is to match them correctly-but crucially, both captions contain a completely identical set of words, only in a different order. The dataset was carefully hand-curated by expert annotators and is labeled with a rich set offine-grained tags to assist in analyzing model performance. We probe a diverse range of state-of-the-art vision and language models and find that, surprisingly, none of them do much better than chance. Evidently, these models are not as skilled at visio-linguistic compositional reasoning as we might have hoped. We perform an extensive analysis to obtain insights into how future work might try to mitigate these models' shortcomings. We aim for Winoground to serve as a useful evaluation set for advancing the state of the art and driving further progress in the field. The dataset is available at https://huggingface.co/datasets/facebook/winoground.