mlsanityData to 7 Oct 2026

SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning

arXiv 2306.12552 ↗First posted 21 Jun 2023cs.CLcited by 3

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Recently, commonsense reasoning in text generation has attracted much attention. Generative commonsense reasoning is the task that requires machines, given a group of keywords, to compose a single coherent sentence with commonsense plausibility. While existing datasets targeting generative commonsense reasoning focus on everyday scenarios, it is unclear how well machines reason under specific geographical and temporal contexts. We formalize this challenging task as SituatedGen, where machines with commonsense should generate a pair of contrastive sentences given a group of keywords including geographical or temporal entities. We introduce a corresponding English dataset consisting of 8,268 contrastive sentence pairs, which are built upon several existing commonsense reasoning benchmarks with minimal manual labor. Experiments show that state-of-the-art generative language models struggle to generate sentences with commonsense plausibility and still lag far behind human performance. Our dataset is publicly available at https://github.com/yunx-z/situated_gen.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 43 of its 62 references that are in the map. First posted on arXiv 21 Jun 2023.

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43 of 62 references.

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Same direction, newest first

  1. 2022Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning21 Oct

Other references · 42

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30 more
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Citing papers per year: 2025: 3.

3 papers.

  1. 2025Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?cited by 36
  2. 2025Foundation Models for Spatio-Temporal Data Science: A Tutorial and Surveycited by 23
  3. 2025Do LLMs exhibit the same commonsense capabilities across languages?cited by 1

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