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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
Answering Unseen Questions With Smaller Language Models Using Rationale Generation and Dense Retrieval—0
Teaching Smaller Language Models To Generalise To Unseen Compositional QuestionsCode0
Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive TasksCode1
Deduction under Perturbed Evidence: Probing Student Simulation Capabilities of Large Language Models—0
Hint of Thought prompting: an explainable and zero-shot approach to reasoning tasks with LLMs—0
Self-Evaluation Guided Beam Search for Reasoning—0
Visconde: Multi-document QA with GPT-3 and Neural RerankingCode1
Distilling Reasoning Capabilities into Smaller Language ModelsCode0
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts—0
Better Retrieval May Not Lead to Better Question Answering—0
PaLM: Scaling Language Modeling with PathwaysCode2
Training Compute-Optimal Large Language ModelsCode6
Self-Consistency Improves Chain of Thought Reasoning in Language ModelsCode1
Scaling Language Models: Methods, Analysis & Insights from Training GopherCode2
Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning StrategiesCode1
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