Papers with Exact Match scores

2 papers
Hybrid Graphs for Table-and-Text based Question Answering using LLMs (2025.naacl-long)

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Challenge: Current methods for QA rely on fine-tuning and high-quality data, which is difficult to obtain.
Approach: They propose a Hybrid Graph-based approach for Table-Text QA that leverages Large Language Models without fine-tuning.
Outcome: The proposed approach improves Exact Match scores by 10% on Hybrid-QA and 5.4% on OTT-QA.
RFiD: Towards Rational Fusion-in-Decoder for Open-Domain Question Answering (2023.findings-acl)

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Challenge: Open-domain Question Answering (ODQA) systems rely on spurious features instead of genuine causal relationships to generate answers.
Approach: They propose a model that leverages the encoders of FiD to distinguish between causal relationships and spurious features and guides the decoder to generate answers informed by this discernment.
Outcome: The proposed model improves on two ODQA datasets and shows that it can identify causal relationships and identify spurious features.

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