SOTAVerified

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

Showing 751–800 of 939 papers

TitleStatusHype
Interactive health insight miner: an adaptive, semantic-based approach—0
Frame- and Entity-Based Knowledge for Common-Sense Argumentative ReasoningCode0
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension—0
Machine Common Sense Concept Paper—0
Towards Robot-Centric Conceptual Knowledge Acquisition—0
A Knowledge Hunting Framework for Common Sense Reasoning—0
HGSGNLP at IEST 2018: An Ensemble of Machine Learning and Deep Neural Architectures for Implicit Emotion Classification in Tweets—0
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking—0
Deep contextualized word representations for detecting sarcasm and ironyCode0
Visual Coreference Resolution in Visual Dialog using Neural Module NetworksCode0
Affordance Extraction and Inference based on Semantic Role Labeling—0
Neural Task Planning with And-Or Graph Representations—0
Inductive Learning of Answer Set Programs from Noisy Examples—0
SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference—0
A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task—0
The Interplay between Lexical Resources and Natural Language ProcessingCode0
Extracting Commonsense Properties from Embeddings with Limited Human GuidanceCode0
Modeling Sentiment Association in Discourse for Humor Recognition—0
Systematic Error Analysis of the Stanford Question Answering Dataset—0
SocialNLP 2018 EmotionX Challenge Overview: Recognizing Emotions in Dialogues—0
Incorporating Chinese Characters of Words for Lexical Sememe PredictionCode0
A Simple Method for Commonsense ReasoningCode0
MITRE at SemEval-2018 Task 11: Commonsense Reasoning without Commonsense Knowledge—0
A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge—0
SemEval-2018 Task 12: The Argument Reasoning Comprehension Task—0
Computational Argumentation: A Journey Beyond Semantics, Logic, Opinions, and Easy Tasks—0
TakeLab at SemEval-2018 Task12: Argument Reasoning Comprehension with Skip-Thought Vectors—0
BLCU\_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention—0
Stacking with Auxiliary Features for Visual Question Answering—0
GIST at SemEval-2018 Task 12: A network transferring inference knowledge to Argument Reasoning Comprehension taskCode0
MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural Network—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
Multimodal Frame Identification with Multilingual Evaluation—0
Visual Relationship Detection Based on Guided Proposals and Semantic Knowledge Distillation—0
VisualBackProp for learning using privileged information with CNNs—0
Event2Mind: Commonsense Inference on Events, Intents, and Reactions—0
Tilde MT Platform for Developing Client Specific MT Solutions—0
Extended HowNet 2.0 -- An Entity-Relation Common-Sense Representation Model—0
A Danish FrameNet Lexicon and an Annotated Corpus Used for Training and Evaluating a Semantic Frame Classifier—0
JFCKB: Japanese Feature Change Knowledge Base—0
A vision-grounded dataset for predicting typical locations for verbs—0
Comprehensive Annotation of Various Types of Temporal Information on the Time Axis—0
Towards Symbolic Reinforcement Learning with Common SenseCode0
DOCK: Detecting Objects by transferring Common-sense Knowledge—0
Empirical Analysis of Foundational Distinctions in Linked Open DataCode0
Fine-grained evaluation of Quality Estimation for Machine translation based on a linguistically motivated Test Suite—0
The Collision of Quality and Technology with Reality—0
Augmented Translation: A New Approach to Combining Human and Machine Capabilities—0
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their InteractionsCode0
Semantic Vector Spaces for Broadening Consideration of Consequences—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ST-MoE-32B 269B (fine-tuned)Accuracy96.1—Unverified
2Unicorn 11B (fine-tuned)Accuracy91.3—Unverified
3CompassMTL 567M with TailorAccuracy90.5—Unverified
4CompassMTL 567MAccuracy89.6—Unverified
5UnifiedQA 11B (fine-tuned)Accuracy89.4—Unverified
6Claude 3 Opus (5-shot)Accuracy88.5—Unverified
7GPT-4 (5-shot)Accuracy87.5—Unverified
8ExDeBERTa 567MAccuracy87—Unverified
9LLaMA-2 13B + MixLoRAAccuracy86.3—Unverified
10LLaMA3 8B+MoSLoRAAccuracy85.8—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4 (few-shot, k=25)Accuracy96.4—Unverified
2PaLM 2 (few-shot, CoT, SC)Accuracy95.1—Unverified
3Shivaay (4B, few-shot, k=8)Accuracy91.04—Unverified
4StupidLLMAccuracy91.03—Unverified
5Claude 2 (few-shot, k=5)Accuracy91—Unverified
6Claude 1.3 (few-shot, k=5)Accuracy90—Unverified
7PaLM 540B (Self Improvement, Self Consistency)Accuracy89.8—Unverified
8PaLM 540B (Self Consistency)Accuracy88.7—Unverified
9PaLM 540B (Self Improvement, CoT Prompting)Accuracy88.3—Unverified
10PaLM 540B (Self Improvement, Standard-Prompting)Accuracy87.2—Unverified
#ModelMetricClaimedVerifiedStatus
1ST-MoE-32B 269B (fine-tuned)Accuracy95.2—Unverified
2LLaMA 3 8B+MoSLoRA (fine-tuned)Accuracy90.5—Unverified
3PaLM 2-L (1-shot)Accuracy89.7—Unverified
4PaLM 2-M (1-shot)Accuracy88—Unverified
5LLaMA-3 8B + MixLoRAAccuracy86.5—Unverified
6Camelidae-8×34BAccuracy86.2—Unverified
7PaLM 2-S (1-shot)Accuracy85.6—Unverified
8LLaMA 65B + CFG (0-shot)Accuracy84.2—Unverified
9GAL 120B (0-shot)Accuracy83.8—Unverified
10LLaMA-2 13B + MixLoRAAccuracy83.5—Unverified
#ModelMetricClaimedVerifiedStatus
1Turing NLR v5 XXL 5.4B (fine-tuned)EM95.9—Unverified
2ST-MoE-32B 269B (fine-tuned)EM95.1—Unverified
3T5-11BF194.1—Unverified
4DeBERTa-1.5BEM94.1—Unverified
5PaLM 540B (finetuned)EM94—Unverified
6Vega v2 6B (fine-tuned)EM93.9—Unverified
7PaLM 2-L (one-shot)F193.8—Unverified
8T5-XXL 11B (fine-tuned)EM93.4—Unverified
9PaLM 2-M (one-shot)F192.4—Unverified
10PaLM 2-S (one-shot)F192.1—Unverified