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

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Showing 826–850 of 939 papers

Title	Date	Tasks	Status
An Aposteriorical Clusterability Criterion for k-Means++ and Simplicity of Clustering	Apr 24, 2017	ClusteringCommon Sense Reasoning	—Unverified
Improving Implicit Semantic Role Labeling by Predicting Semantic Frame Arguments	Apr 10, 2017	Common Sense ReasoningSemantic Role Labeling	—Unverified
A Service-Oriented Architecture for Assisting the Authoring of Semantic Crowd Maps	Apr 6, 2017	Common Sense Reasoning	—Unverified
IIT (BHU): System Description for LSDSem'17 Shared Task	Apr 1, 2017	Cloze TestCommon Sense Reasoning	—Unverified
Aspect Extraction from Product Reviews Using Category Hierarchy Information	Apr 1, 2017	Aspect ExtractionCommon Sense Reasoning	—Unverified
Behind the Scenes of an Evolving Event Cloze Test	Apr 1, 2017	Cloze TestCommon Sense Reasoning	—Unverified
Probabilistic Inference for Cold Start Knowledge Base Population with Prior World Knowledge	Apr 1, 2017	Common Sense ReasoningEntity Linking	—Unverified
TTCS^: a Vectorial Resource for Computing Conceptual Similarity	Apr 1, 2017	Common Sense Reasoning	—Unverified
Cooperating with Machines	Mar 17, 2017	Common Sense ReasoningFace Recognition	—Unverified
Symbol Grounding via Chaining of Morphisms	Mar 13, 2017	Common Sense Reasoning	—Unverified
Strongly-Typed Agents are Guaranteed to Interact Safely	Feb 24, 2017	blind source separationCommon Sense Reasoning	—Unverified
Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction	Feb 10, 2017	Common Sense ReasoningPrediction	—Unverified
Investigating the Application of Common-Sense Knowledge-Base for Identifying Term Obfuscation in Adversarial Communication	Jan 18, 2017	Common Sense ReasoningSentence	—Unverified
Minimally Naturalistic Artificial Intelligence	Jan 14, 2017	Common Sense ReasoningInductive Bias	—Unverified
Handling Multiword Expressions in Causality Estimation	Jan 1, 2017	Common Sense ReasoningQuestion Answering	—Unverified
An Evaluation of PredPatt and Open IE via Stage 1 Semantic Role Labeling	Jan 1, 2017	Common Sense ReasoningOpen Information Extraction	CodeCode Available
Quantifier Scoping and Semantic Preferences	Jan 1, 2017	Common Sense ReasoningNatural Language Inference	—Unverified
Correcting Contradictions	Jan 1, 2017	Common Sense ReasoningNatural Language Inference	CodeCode Available
Ambiguss, a game for building a Sense Annotated Corpus for French	Jan 1, 2017	Common Sense Reasoning	—Unverified
Large-Scale Acquisition of Commonsense Knowledge via a Quiz Game on a Dialogue System	Dec 1, 2016	Common Sense ReasoningQuestion Answering	—Unverified
Incremental Fine-grained Information Status Classification Using Attention-based LSTMs	Dec 1, 2016	ClassificationCommon Sense Reasoning	—Unverified
Automatic Evaluation of Commonsense Knowledge for Refining Japanese ConceptNet	Dec 1, 2016	Common Sense Reasoning	—Unverified
Learning from Maps: Visual Common Sense for Autonomous Driving	Nov 25, 2016	Autonomous DrivingAutonomous Vehicles	—Unverified
Ordinal Common-sense Inference	Nov 2, 2016	Common Sense ReasoningNatural Language Inference	—Unverified
Resolving Language and Vision Ambiguities Together: Joint Segmentation \& Prepositional Attachment Resolution in Captioned Scenes	Nov 1, 2016	Common Sense ReasoningPrepositional Phrase Attachment	—Unverified

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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