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StrategyQA
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StrategyQA aims to measure the ability of models to answer questions that require multi-step implicit reasoning.
Source: BIG-bench
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Rule-Guided Feedback: Enhancing Reasoning by Enforcing Rule Adherence in Large Language Models
DeLTa: A Decoding Strategy based on Logit Trajectory Prediction Improves Factuality and Reasoning Ability
Voting or Consensus? Decision-Making in Multi-Agent Debate
Unraveling Indirect In-Context Learning Using Influence Functions
AutoReason: Automatic Few-Shot Reasoning Decomposition
Dialectical Behavior Therapy Approach to LLM Prompting
Rationale-Aware Answer Verification by Pairwise Self-Evaluation
A Looming Replication Crisis in Evaluating Behavior in Language Models? Evidence and Solutions
Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning
Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
Question-Analysis Prompting Improves LLM Performance in Reasoning Tasks
Meta-prompting Optimized Retrieval-augmented Generation
Advancing Process Verification for Large Language Models via Tree-Based Preference Learning
Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast
Improving Attributed Text Generation of Large Language Models via Preference Learning
CR-LT-KGQA: A Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail Knowledge
Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and Distillation
Towards Uncertainty-Aware Language Agent
Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning
IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions
The ART of LLM Refinement: Ask, Refine, and Trust
Tailoring Self-Rationalizers with Multi-Reward Distillation
Improving Planning with Large Language Models: A Modular Agentic Architecture
Large Language Models Are Also Good Prototypical Commonsense Reasoners
Answering Unseen Questions With Smaller Language Models Using Rationale Generation and Dense Retrieval
Teaching Smaller Language Models To Generalise To Unseen Compositional Questions
Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks
Deduction under Perturbed Evidence: Probing Student Simulation Capabilities of Large Language Models
Hint of Thought prompting: an explainable and zero-shot approach to reasoning tasks with LLMs
Self-Evaluation Guided Beam Search for Reasoning
Visconde: Multi-document QA with GPT-3 and Neural Reranking
Distilling Reasoning Capabilities into Smaller Language Models
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts
Better Retrieval May Not Lead to Better Question Answering
PaLM: Scaling Language Modeling with Pathways
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