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Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies

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arxiv 2101.02235 v1 pith:3ELDSIUX submitted 2021-01-06 cs.CL

classification cs.CL
keywords questionreasoningansweringstepsstrategiesstrategyqabenchmarkdecomposition
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

A key limitation in current datasets for multi-hop reasoning is that the required steps for answering the question are mentioned in it explicitly. In this work, we introduce StrategyQA, a question answering (QA) benchmark where the required reasoning steps are implicit in the question, and should be inferred using a strategy. A fundamental challenge in this setup is how to elicit such creative questions from crowdsourcing workers, while covering a broad range of potential strategies. We propose a data collection procedure that combines term-based priming to inspire annotators, careful control over the annotator population, and adversarial filtering for eliminating reasoning shortcuts. Moreover, we annotate each question with (1) a decomposition into reasoning steps for answering it, and (2) Wikipedia paragraphs that contain the answers to each step. Overall, StrategyQA includes 2,780 examples, each consisting of a strategy question, its decomposition, and evidence paragraphs. Analysis shows that questions in StrategyQA are short, topic-diverse, and cover a wide range of strategies. Empirically, we show that humans perform well (87%) on this task, while our best baseline reaches an accuracy of $\sim$66%.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 18 citations worldwide. Full citation record

  1. Efficient Reasoning on the Edge

    cs.LG 2026-03 accept novelty 5.5 of 10

    LoRA adapters, budget-forced GRPO, dynamic switching, parallel verification and FPTQuant enable practical chain-of-thought reasoning on quantized Qwen2.5-7B for edge devices.

  2. Multilingual Test-Time Scaling via Initial Thought Transfer

    cs.CL 2025-05 reject novelty 5.0 of 10

    MITT, a prefix-tuning method for multilingual test-time scaling, is evaluated on questions whose English reasoning was used for training, confounding the reported gains.

  3. Error Reflection Prompting: Can Large Language Models Successfully Understand Errors?

    cs.CL 2025-08 unverdicted novelty 4.0 of 10

    Error Reflection Prompting, a chain-of-thought variant that includes an incorrect answer and error recognition, is claimed to improve LLM reasoning performance and interpretability.

  4. Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategies

    cs.CL 2025-07 reject novelty 4.0 of 10

    MoR fine-tunes Qwen2.5 on GPT-4o-selected reasoning templates, claiming up to 13.5% accuracy gains, but the reported gains are not robustly supported.

  5. Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey

    cs.AI 2025-07 conditional novelty 3.0 of 10

    A comprehensive review that categorizes methods for shortening and adaptively triggering chain-of-thought reasoning in large language models.

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