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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
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
Question-Analysis Prompting Improves LLM Performance in Reasoning Tasks—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
DeLTa: A Decoding Strategy based on Logit Trajectory Prediction Improves Factuality and Reasoning AbilityCode0
Rationale-Aware Answer Verification by Pairwise Self-EvaluationCode0
Distilling Reasoning Capabilities into Smaller Language ModelsCode0
Tailoring Self-Rationalizers with Multi-Reward DistillationCode0
Teaching Smaller Language Models To Generalise To Unseen Compositional QuestionsCode0
Voting or Consensus? Decision-Making in Multi-Agent DebateCode0
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