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StrategyQA

StrategyQA aims to measure the ability of models to answer questions that require multi-step implicit reasoning.

Source: BIG-bench

Papers

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Showing 21–30 of 40 papers

TitleStatusHype
Better Retrieval May Not Lead to Better Question Answering—0
Deduction under Perturbed Evidence: Probing Student Simulation Capabilities of Large Language Models—0
Dialectical Behavior Therapy Approach to LLM Prompting—0
Fusing Bidirectional Chains of Thought and Reward Mechanisms A Method for Enhancing Question-Answering Capabilities of Large Language Models for Chinese Intangible Cultural Heritage—0
IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions—0
Improving Attributed Text Generation of Large Language Models via Preference Learning—0
Large Language Models Are Also Good Prototypical Commonsense Reasoners—0
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts—0
Meta-prompting Optimized Retrieval-augmented Generation—0
Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning—0
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