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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 11–20 of 40 papers

TitleStatusHype
Mutual Reasoning Makes Smaller LLMs Stronger Problem-SolversCode4
Meta-prompting Optimized Retrieval-augmented Generation—0
Question-Analysis Prompting Improves LLM Performance in Reasoning Tasks—0
Advancing Process Verification for Large Language Models via Tree-Based Preference Learning—0
Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-ContrastCode1
Improving Attributed Text Generation of Large Language Models via Preference Learning—0
CR-LT-KGQA: A Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail KnowledgeCode1
Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and DistillationCode1
Towards Uncertainty-Aware Language Agent—0
Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step ReasoningCode1
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