StrategyQA

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

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Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

20 shown of 20 papers with code (40 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• PaLM: Scaling Language Modeling with Pathways
  5 Apr 2022 7 repositories listed Syntology ran 30 of 37 samples · 7 unverified
  To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM.
• Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
  12 Aug 2024 3 repositories listed Syntology ran 1 of 2 samples · 1 unverified
  This paper introduces rStar, a self-play mutual reasoning approach that significantly improves reasoning capabilities of small language models (SLMs) without fine-tuning or superior models.
• Improving Planning with Large Language Models: A Modular Agentic Architecture
  30 Sep 2023 3 repositories listed
  To improve planning with LLMs, we propose an agentic architecture, the Modular Agentic Planner (MAP), in which planning is accomplished via the recurrent interaction of the specialized modules mentioned above, each…
• Self-Consistency Improves Chain of Thought Reasoning in Language Models
  21 Mar 2022 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  Chain-of-thought prompting combined with pre-trained large language models has achieved encouraging results on complex reasoning tasks.
• Scaling Language Models: Methods, Analysis & Insights from Training Gopher
  8 Dec 2021 3 repositories listed
  Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
• Training Compute-Optimal Large Language Models
  29 Mar 2022 2 repositories listed Syntology ran 8 of 11 samples · 3 unverified · 4 pointer-only (licence)
  We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
• DeLTa: A Decoding Strategy based on Logit Trajectory Prediction Improves Factuality and Reasoning Ability
  4 Mar 2025 1 repository listed
  Large Language Models (LLMs) are increasingly being used in real-world applications.
• Voting or Consensus? Decision-Making in Multi-Agent Debate
  26 Feb 2025 1 repository listed
  Our results show that voting protocols improve performance by 13.
• AutoReason: Automatic Few-Shot Reasoning Decomposition
  9 Dec 2024 1 repository listed
  Chain of Thought (CoT) was introduced in recent research as a method for improving step-by-step reasoning in Large Language Models.
• Rationale-Aware Answer Verification by Pairwise Self-Evaluation
  7 Oct 2024 1 repository listed Syntology ran 6 of 7 samples · 1 unverified
  Furthermore, we demonstrate that training a verifier on valid rationales significantly improves its ability to distinguish valid and flawed rationale.
• Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast
  23 May 2024 1 repository listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  In MoE, each token in the input sequence activates a different subset of experts determined by a routing mechanism.
• CR-LT-KGQA: A Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail Knowledge
  3 Mar 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  In this work, we seek a novel KGQA dataset that supports commonsense reasoning and focuses on long-tail entities (e.
• Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and Distillation
  21 Feb 2024 1 repository listed Syntology ran 6 of 10 samples · 4 unverified · 10 pointer-only (licence)
  We propose a straightforward approach called Distillation Contrastive Decoding (DCD) to enhance the reasoning capabilities of Large Language Models (LLMs) during inference.
• Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning
  19 Jan 2024 1 repository listed Syntology ran 7 of 8 samples · 1 unverified · 8 pointer-only (licence)
  Self-consistency (SC) has been a widely used decoding strategy for chain-of-thought reasoning.
• Tailoring Self-Rationalizers with Multi-Reward Distillation
  6 Nov 2023 1 repository listed Syntology ran 6 of 10 samples · 4 unverified · 10 pointer-only (licence)
  Results on five difficult question-answering datasets StrategyQA, QuaRel, OpenBookQA, NumerSense and QASC show that not only does MaRio improve task accuracy, but it also improves the self-rationalization quality of…
• Teaching Smaller Language Models To Generalise To Unseen Compositional Questions
  2 Aug 2023 1 repository listed
  We equip a smaller Language Model to generalise to answering challenging compositional questions that have not been seen in training.
• Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks
  28 May 2023 1 repository listed Syntology ran 2 of 6 samples · 4 unverified
  Large Language Models (LLMs) have shown promising performance in knowledge-intensive reasoning tasks that require a compound understanding of knowledge.
• Visconde: Multi-document QA with GPT-3 and Neural Reranking
  19 Dec 2022 1 repository listed
  This paper proposes a question-answering system that can answer questions whose supporting evidence is spread over multiple (potentially long) documents.
• Distilling Reasoning Capabilities into Smaller Language Models
  1 Dec 2022 1 repository listed
  In this work, we propose an alternative reasoning scheme, Socratic CoT, that learns a decomposition of the original problem into a sequence of subproblems and uses it to guide the intermediate reasoning steps.
• Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies
  6 Jan 2021 1 repository listed
  A key limitation in current datasets for multi-hop reasoning is that the required steps for answering the question are mentioned in it explicitly.

Syntology lines on 11 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.