Broadly applicable and accurate protein design by integrating structure prediction networks (RFDiffusion)
Abstract
Temporal reasoning is the task of predicting temporal relations of event pairs. While temporal reasoning models can perform reasonably well on in-domain benchmarks, we have little idea of these systems' generalizability due to existing datasets' limitations. In this work, we introduce a novel task named TODAY that bridges this gap with temporal differential analysis, which as the name suggests, evaluates whether systems can correctly understand the effect of incremental changes. Specifically, TODAY introduces slight contextual changes for given event pairs, and systems are asked to tell how this subtle contextual change would affect relevant temporal relation distributions. To facilitate learning, TODAY also annotates human explanations. We show that existing models, including GPT-3.5, drop to random guessing on TODAY, suggesting that they heavily rely on spurious information rather than proper reasoning for temporal predictions. On the other hand, we show that TODAY's supervision style and explanation annotations can be used in joint learning, encouraging models to use more appropriate signals during training and thus outperform across several benchmarks. TODAY can also be used to train models to solicit incidental supervision from noisy sources such as GPT-3.5, thus moving us more toward the goal of generic temporal reasoning systems.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of the TODAY task, which enhances the understanding of temporal reasoning through differential analysis, highlighting the limitations of current models and proposing a novel approach to improve their performance. This work has the potential to influence future research in temporal reasoning and related fields significantly.
Methodology
The paper introduces a novel task called TODAY, which focuses on temporal reasoning through differential analysis. This approach is innovative as it emphasizes the understanding of incremental changes in context and their effects on temporal relations, a gap that existing models have not adequately addressed. The methodology includes human explanations as annotations, which is a unique aspect that enhances the learning process. However, the paper could benefit from a more detailed description of the model architecture and training procedures used to achieve the reported results.
Results & evaluation
The experiments demonstrate that existing models, including GPT-3.5, struggle with the TODAY task, indicating the limitations of current temporal reasoning systems. The paper provides a clear evaluation framework, showing how the proposed supervision style and explanation annotations improve model performance across various benchmarks. However, the results could be strengthened with a broader range of models and more extensive ablation studies to isolate the effects of the proposed methods.
Reproducibility
The paper lacks sufficient implementation details and code availability, which are critical for reproducibility. While the results are promising, without access to the code or a clear description of the experimental setup, it is challenging for other researchers to replicate the findings or build upon this work.
Limitations
The paper acknowledges some limitations, such as the reliance on existing models that may not generalize well and the potential for spurious correlations in the data. Additionally, the task of temporal reasoning is complex, and the proposed method may not cover all aspects of temporal relations, which could limit its applicability in real-world scenarios.
Broader impact
The implications of this work are significant, as it addresses a fundamental challenge in temporal reasoning, which is crucial for various applications, including natural language understanding, event prediction, and automated reasoning systems. By improving the understanding of temporal relations, this research could lead to advancements in AI systems that require robust reasoning capabilities. The main contribution of this paper is the introduction of the TODAY task, which enhances the understanding of temporal reasoning through differential analysis, highlighting the limitations of current models and proposing a novel approach to improve their performance. This work has the potential to influence future research in temporal reasoning and related fields significantly.
Curator note
Watson et al.; David Baker lab; generative protein design
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