StrategyQA
13 papers with code • 0 benchmarks • 0 datasets
StrategyQA aims to measure the ability of models to answer questions that require multi-step implicit reasoning.
Source: BIG-bench
Benchmarks
These leaderboards are used to track progress in StrategyQA
Most implemented papers
Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning
Self-consistency (SC) has been a widely used decoding strategy for chain-of-thought reasoning.
Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and Distillation
We propose a straightforward approach called Distillation Contrastive Decoding (DCD) to enhance the reasoning capabilities of Large Language Models (LLMs) during inference.
CR-LT-KGQA: A Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail Knowledge
In this work, we seek a novel KGQA dataset that supports commonsense reasoning and focuses on long-tail entities (e. g., non-mainstream and recent entities) where LLMs frequently hallucinate, and thus create the need for novel methodologies that leverage the KG for factual and attributable commonsense inference.
