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

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Showing 701–750 of 939 papers

TitleStatusHype
Multimodal Frame Identification with Multilingual Evaluation—0
Multimodal Sentiment Analysis with Common-sense Modulation—0
Multi Task Inverse Reinforcement Learning for Common Sense Reward—0
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction—0
Multi-turn Response Selection with Commonsense-enhanced Language Models—0
Mxgra at SemEval-2020 Task 4: Common Sense Making with Next Token Prediction—0
Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension—0
NaturalLI: Natural Logic Inference for Common Sense Reasoning—0
Navigating Semantic Relations: Challenges for Language Models in Abstract Common-Sense Reasoning—0
Neural NID Rules—0
Neural Task Planning with And-Or Graph Representations—0
Neuro-Symbolic Learning: Principles and Applications in Ophthalmology—0
NEWSKVQA: Knowledge-Aware News Video Question Answering—0
NEWTON: Are Large Language Models Capable of Physical Reasoning?—0
NLP@JUST at SemEval-2020 Task 4: Ensemble Technique for BERT and Roberta to Evaluate Commonsense Validation—0
Non-Monotonic Reasoning and Story Comprehension—0
Not-so fine-tuning: Measures of Common Sense for Language Models—0
NTSEBENCH: Cognitive Reasoning Benchmark for Vision Language Models—0
Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare—0
Online Knowledge Integration for 3D Semantic Mapping: A Survey—0
Online learnability of Statistical Relational Learning in anomaly detection—0
On Reality and the Limits of Language Data: Aligning LLMs with Human Norms—0
On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations—0
On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations—0
On the Multiple Roles of Ontologies in Explainable AI—0
On Utilizing Relationships for Transferable Few-Shot Fine-Grained Object Detection—0
Open Information Extraction for Spanish Language based on Syntactic Constraints—0
OPI: Semeval-2014 Task 3 System Description—0
Orca 2: Teaching Small Language Models How to Reason—0
Ordinal Common-sense Inference—0
OSoRA: Output-Dimension and Singular-Value Initialized Low-Rank Adaptation—0
PaLM 2 Technical Report—0
Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack—0
PASTA: A Dataset for Modeling Participant States in Narratives—0
Path-Consistency: Prefix Enhancement for Efficient Inference in LLM—0
Penetrative AI: Making LLMs Comprehend the Physical World—0
Perplexity from PLM Is Unreliable for Evaluating Text Quality—0
Personalized Causal Graph Reasoning for LLMs: A Case Study on Dietary Recommendations—0
Philosophers are Mortal: Inferring the Truth of Unseen Facts—0
PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding—0
Picturing Ambiguity: A Visual Twist on the Winograd Schema Challenge—0
Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks—0
Planning Automated Driving with Accident Experience Referencing and Common-sense Inferencing—0
CAPE: Corrective Actions from Precondition Errors using Large Language Models—0
Plant in Cupboard, Orange on Rably, Inat Aphone. Benchmarking Incremental Learning of Situation and Language Model using a Text-Simulated Situated Environment—0
Plants Don't Walk on the Street: Common-Sense Reasoning for Reliable Semantic Segmentation—0
Playing Text-Based Games with Common Sense—0
PlugMed: Improving Specificity in Patient-Centered Medical Dialogue Generation using In-Context Learning—0
Potential and Limits of Using Post-edits as Reference Translations for MT Evaluation—0
Predicting Numerals in Natural Language Text Using a Language Model Considering the Quantitative Aspects of Numerals—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