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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 26–40 of 40 papers

TitleStatusHype
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
Advancing Process Verification for Large Language Models via Tree-Based Preference Learning—0
Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning—0
Question-Analysis Prompting Improves LLM Performance in Reasoning Tasks—0
Rule-Guided Feedback: Enhancing Reasoning by Enforcing Rule Adherence in Large Language Models—0
Self-Evaluation Guided Beam Search for Reasoning—0
Hint of Thought prompting: an explainable and zero-shot approach to reasoning tasks with LLMs—0
The ART of LLM Refinement: Ask, Refine, and Trust—0
Towards Uncertainty-Aware Language Agent—0
Unraveling Indirect In-Context Learning Using Influence Functions—0
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