Summary of Mdcr: a Dataset For Multi-document Conditional Reasoning, by Peter Baile Chen et al.
MDCR: A Dataset for Multi-Document Conditional Reasoning
by Peter Baile Chen, Yi Zhang, Chunwei Liu, Sejal Gupta, Yoon Kim, Michael Cafarella
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The ConditionalQA model is designed to evaluate a machine learning model’s ability to answer eligibility questions based on a single document, considering unmentioned conditions. However, the authors acknowledge that this approach has limitations when it comes to more challenging questions that require cross-document reasoning and optimization. To address this limitation, they propose a new dataset called MDCR that reflects real-world challenges and serves as a test bed for complex conditional reasoning tasks. The dataset is evaluated using recent large language models (LLMs), which demonstrate their limitations in solving these types of optimization problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to evaluate machine learning models’ ability to answer questions based on multiple documents, taking into account unknown conditions and optimization requirements. The ConditionalQA model can only handle single-document questions, so the authors introduce the MDCR dataset to test models’ capabilities in more complex scenarios. This research aims to improve our understanding of how well current language models can solve real-world problems. |
Keywords
» Artificial intelligence » Machine learning » Optimization
