Common Sense Reasoning

Common sense reasoning tasks are intended to require the model to go beyond pattern recognition. Instead, the model should use "common sense" or world knowledge to make inferences.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 701–725 of 939 papers

Title	Date	Tasks	Status
Multimodal Frame Identification with Multilingual Evaluation	Jun 1, 2018	Common Sense ReasoningSemantic Role Labeling	—Unverified
Multimodal Sentiment Analysis with Common-sense Modulation	Nov 16, 2021	Common Sense ReasoningMultimodal Sentiment Analysis	—Unverified
Multi Task Inverse Reinforcement Learning for Common Sense Reward	Feb 17, 2024	Common Sense Reasoningreinforcement-learning	—Unverified
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction	Jun 17, 2021	Common Sense ReasoningEmotion Cause Extraction	—Unverified
Multi-turn Response Selection with Commonsense-enhanced Language Models	Jul 26, 2024	Common Sense ReasoningGraph Neural Network	—Unverified
Mxgra at SemEval-2020 Task 4: Common Sense Making with Next Token Prediction	Dec 1, 2020	Common Sense ReasoningLanguage Modeling	—Unverified
Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension	Dec 13, 2021	ArticlesCommon Sense Reasoning	—Unverified
NaturalLI: Natural Logic Inference for Common Sense Reasoning	Oct 1, 2014	Common Sense Reasoning	—Unverified
Navigating Semantic Relations: Challenges for Language Models in Abstract Common-Sense Reasoning	Feb 19, 2025	Common Sense ReasoningMathematical Problem-Solving	—Unverified
Neural NID Rules	Feb 12, 2022	Common Sense ReasoningGraph Neural Network	—Unverified
Neural Task Planning with And-Or Graph Representations	Aug 25, 2018	Common Sense ReasoningTask Planning	—Unverified
Neuro-Symbolic Learning: Principles and Applications in Ophthalmology	Jul 31, 2022	Common Sense ReasoningImage Captioning	—Unverified
NEWSKVQA: Knowledge-Aware News Video Question Answering	Feb 8, 2022	Common Sense ReasoningManagement	—Unverified
NEWTON: Are Large Language Models Capable of Physical Reasoning?	Oct 10, 2023	AttributeCommon Sense Reasoning	—Unverified
NLP@JUST at SemEval-2020 Task 4: Ensemble Technique for BERT and Roberta to Evaluate Commonsense Validation	Dec 1, 2020	Common Sense Reasoning	—Unverified
Non-Monotonic Reasoning and Story Comprehension	Jul 14, 2014	Common Sense ReasoningReading Comprehension	—Unverified
Not-so fine-tuning: Measures of Common Sense for Language Models	Sep 29, 2021	Common Sense ReasoningGPU	—Unverified
NTSEBENCH: Cognitive Reasoning Benchmark for Vision Language Models	Jul 15, 2024	Common Sense ReasoningMultiple-choice	—Unverified
Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare	Oct 10, 2024	Common Sense ReasoningData Augmentation	—Unverified
Online Knowledge Integration for 3D Semantic Mapping: A Survey	Nov 27, 2024	Common Sense ReasoningKnowledge Graphs	—Unverified
Online learnability of Statistical Relational Learning in anomaly detection	May 18, 2017	Anomaly DetectionCommon Sense Reasoning	—Unverified
On Reality and the Limits of Language Data: Aligning LLMs with Human Norms	Aug 25, 2022	Common Sense Reasoning	—Unverified
On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations	Jan 16, 2022	Common Sense ReasoningWorld Knowledge	—Unverified
On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations	Apr 27, 2022	Common Sense ReasoningWorld Knowledge	—Unverified
On the Multiple Roles of Ontologies in Explainable AI	Nov 8, 2023	Common Sense ReasoningManagement	—Unverified

Show:10 25 50

← PrevPage 29 of 38Next →

All datasets WinoGrande arc_challenge arc_easy ReCoRD CommonsenseQA PARus RuCoS RWSD BIG-bench (Causal Judgment)BIG-bench (Date Understanding)BIG-bench (Disambiguation QA)BIG-bench (Sports Understanding)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ST-MoE-32B 269B (fine-tuned)	Accuracy	96.1	—	Unverified
2	Unicorn 11B (fine-tuned)	Accuracy	91.3	—	Unverified
3	CompassMTL 567M with Tailor	Accuracy	90.5	—	Unverified
4	CompassMTL 567M	Accuracy	89.6	—	Unverified
5	UnifiedQA 11B (fine-tuned)	Accuracy	89.4	—	Unverified
6	Claude 3 Opus (5-shot)	Accuracy	88.5	—	Unverified
7	GPT-4 (5-shot)	Accuracy	87.5	—	Unverified
8	ExDeBERTa 567M	Accuracy	87	—	Unverified
9	LLaMA-2 13B + MixLoRA	Accuracy	86.3	—	Unverified
10	LLaMA3 8B+MoSLoRA	Accuracy	85.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GPT-4 (few-shot, k=25)	Accuracy	96.4	—	Unverified
2	PaLM 2 (few-shot, CoT, SC)	Accuracy	95.1	—	Unverified
3	Shivaay (4B, few-shot, k=8)	Accuracy	91.04	—	Unverified
4	StupidLLM	Accuracy	91.03	—	Unverified
5	Claude 2 (few-shot, k=5)	Accuracy	91	—	Unverified
6	Claude 1.3 (few-shot, k=5)	Accuracy	90	—	Unverified
7	PaLM 540B (Self Improvement, Self Consistency)	Accuracy	89.8	—	Unverified
8	PaLM 540B (Self Consistency)	Accuracy	88.7	—	Unverified
9	PaLM 540B (Self Improvement, CoT Prompting)	Accuracy	88.3	—	Unverified
10	PaLM 540B (Self Improvement, Standard-Prompting)	Accuracy	87.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ST-MoE-32B 269B (fine-tuned)	Accuracy	95.2	—	Unverified
2	LLaMA 3 8B+MoSLoRA (fine-tuned)	Accuracy	90.5	—	Unverified
3	PaLM 2-L (1-shot)	Accuracy	89.7	—	Unverified
4	PaLM 2-M (1-shot)	Accuracy	88	—	Unverified
5	LLaMA-3 8B + MixLoRA	Accuracy	86.5	—	Unverified
6	Camelidae-8×34B	Accuracy	86.2	—	Unverified
7	PaLM 2-S (1-shot)	Accuracy	85.6	—	Unverified
8	LLaMA 65B + CFG (0-shot)	Accuracy	84.2	—	Unverified
9	GAL 120B (0-shot)	Accuracy	83.8	—	Unverified
10	LLaMA-2 13B + MixLoRA	Accuracy	83.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Turing NLR v5 XXL 5.4B (fine-tuned)	EM	95.9	—	Unverified
2	ST-MoE-32B 269B (fine-tuned)	EM	95.1	—	Unverified
3	T5-11B	F1	94.1	—	Unverified
4	DeBERTa-1.5B	EM	94.1	—	Unverified
5	PaLM 540B (finetuned)	EM	94	—	Unverified
6	Vega v2 6B (fine-tuned)	EM	93.9	—	Unverified
7	PaLM 2-L (one-shot)	F1	93.8	—	Unverified
8	T5-XXL 11B (fine-tuned)	EM	93.4	—	Unverified
9	PaLM 2-M (one-shot)	F1	92.4	—	Unverified
10	PaLM 2-S (one-shot)	F1	92.1	—	Unverified