Papers with reasoning path selection

2 papers
Pay More Attention to Relation Exploration for Knowledge Base Question Answering (2023.findings-acl)

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Challenge: Existing approaches focus on entity representation and final answer reasoning, which results in limited supervision for this task.
Approach: They propose a framework that utilizes relations to enhance entity representation and introduce additional supervision.
Outcome: The proposed framework improves the F1 score on two benchmark datasets by 5.8% . it improves by 6.7% on WebQSP, better than state-of-the-art methods .
How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled Benchmark (2025.emnlp-main)

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Challenge: Prior work has not explored the mechanisms underlying this sensitivity.
Approach: They propose a synthetic benchmark to evaluate Large Language Models’ reasoning robustness against systematically controlled irrelevant context (IC).
Outcome: The proposed model improves in-distribution and out-of-disttribution scenarios while training with strong distractors.

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