mlsanityData to 14 Oct 2026

QASC: A Dataset for Question Answering via Sentence Composition

arXiv 1910.11473 ↗First posted 25 Oct 2019cs.CLcited by 252

Not on the map. Why?

Cited by 52 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Composing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition(QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are annotated in a large corpus, and (b) the decomposition into these facts is not evident from the question itself. The latter makes retrieval challenging as the system must introduce new concepts or relations in order to discover potential decompositions. Further, the reasoning model must then learn to identify valid compositions of these retrieved facts using common-sense reasoning. To help address these challenges, we provide annotation for supporting facts as well as their composition. Guided by these annotations, we present a two-step approach to mitigate the retrieval challenges. We use other multiple-choice datasets as additional training data to strengthen the reasoning model. Our proposed approach improves over current state-of-the-art language models by 11% (absolute). The reasoning and retrieval problems, however, remain unsolved as this model still lags by 20% behind human performance.

Key facts. Cited by 252 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Oct 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 37; 2022: 46; 2023: 50; 2024: 40; 2025: 48; 2026: 31.

252 papers.

Sort:

By area:

  1. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
  2. 2023A Survey of Large Language Modelscited by 2,576
  3. 2022Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answeringcited by 1,919
  4. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,434
  5. 2022Editing Models with Task Arithmeticcited by 1,120
  6. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 916
  7. 2023TIES-Merging: Resolving Interference When Merging Modelscited by 818
  8. 2022Galactica: A Large Language Model for Sciencecited by 744
  9. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 743
  10. 2021Cross-Task Generalization via Natural Language Crowdsourcing Instructionscited by 699
  11. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 688
  12. 2021MetaICL: Learning to Learn In Contextcited by 454
38 more
  1. 2022ScienceWorld: Is your Agent Smarter than a 5th Grader?cited by 327
  2. 2022UL2: Unifying Language Learning Paradigmscited by 295
  3. 2022OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalizationcited by 236
  4. 2024Continual Learning of Large Language Models: A Comprehensive Surveycited by 234
  5. 2023Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Modelscited by 221
  6. 2021Generated Knowledge Prompting for Commonsense Reasoningcited by 214
  7. 2022Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Modelscited by 180
  8. 2021Reframing Instructional Prompts to GPTk's Languagecited by 176
  9. 2022Large Language Models with Controllable Working Memorycited by 169
  10. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
  11. 2021Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformercited by 134
  12. 2023Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Maskscited by 133
  13. 2024Localizing Task Information for Improved Model Merging and Compressioncited by 131
  14. 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
  15. 2025Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketchingcited by 117
  16. 2024Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoningcited by 110
  17. 2023The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuningcited by 108
  18. 2023Encyclopedic VQA: Visual questions about detailed properties of fine-grained categoriescited by 107
  19. 2023SCOTT: Self-Consistent Chain-of-Thought Distillationcited by 105
  20. 2024Small Language Models Need Strong Verifiers to Self-Correct Reasoningcited by 86
  21. 2021Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challengecited by 80
  22. 2023Exploring the Benefits of Training Expert Language Models over Instruction Tuningcited by 80
  23. 2024Parameter Competition Balancing for Model Mergingcited by 72
  24. 2022Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Modelscited by 70
  25. 2024Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for Reasoningcited by 69
  26. 2023ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Modelscited by 63
  27. 2024Instruction Pre-Training: Language Models are Supervised Multitask Learnerscited by 63
  28. 2023Natural Language Reasoning, A Surveycited by 61
  29. 2025Efficient Inference for Large Reasoning Models: A Surveycited by 61
  30. 2023Don't throw away your value model! Generating more preferable text with Value-Guided Monte-Carlo Tree Search decodingcited by 60
  31. 2021ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoningcited by 58
  32. 2022UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Trainingcited by 57
  33. 2022Improving In-Context Few-Shot Learning via Self-Supervised Trainingcited by 57
  34. 2022PINTO: Faithful Language Reasoning Using Prompt-Generated Rationalescited by 56
  35. 2024Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Applicationcited by 56
  36. 2024CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation for Evaluating LLMs in Cybersecurity Knowledgecited by 55
  37. 2023Active Learning Principles for In-Context Learning with Large Language Modelscited by 54
  38. 2025Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Modelscited by 48

50 most cited of 252 All 252 (JSON)