Papers by Mike Cafarella

2 papers
MDCR: A Dataset for Multi-Document Conditional Reasoning (2024.findings-emnlp)

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Challenge: ConditionalQA is limited to questions on single documents, neglecting harder cases that may require *cross-document reasoning* and *optimization*.
Approach: They propose to use a dataset to evaluate models' ability to answer eligibility questions on single documents.
Outcome: The proposed dataset can reflect real-world challenges and serve as a test bed for complex conditional reasoning that requires optimization.
Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method (2025.acl-long)

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Challenge: Existing RAG solutions address the alignment problem in a limited manner . ARM explores relationships among data objects, enabling a retrieve-all-at-once solution for complex queries .
Approach: Experimental results show that ARM improves alignment of open-domain questions with available data . ARM explores relationships among data objects, enabling a retrieve-all-at-once solution for complex queries.
Outcome: Experimental results show that ARM outperforms existing RAG methods on complex open-domain questions.

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