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 351–400 of 939 papers

TitleStatusHype
ADEPT: An Adjective-Dependent Plausibility Task—0
Generating Interactive Worlds with Text—0
Fine-grained evaluation of Quality Estimation for Machine translation based on a linguistically motivated Test Suite—0
Generating Out-Of-Distribution Scenarios Using Language Models—0
Interactive health insight miner: an adaptive, semantic-based approach—0
FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering—0
Features of Verb Complements in Co-composition: A case study of Chinese baking verb using Weibo corpus—0
Geo-distinctive Visual Element Matching for Location Estimation of Images—0
Combining PCFG-LA Models with Dual Decomposition: A Case Study with Function Labels and Binarization—0
Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments—0
Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes—0
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts—0
Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios—0
Extraction of Common-Sense Relations from Procedural Task Instructions using BERT—0
GLRD: Global-Local Collaborative Reason and Debate with PSL for 3D Open-Vocabulary Detection—0
GO FIGURE: A Meta Evaluation of Factuality in Summarization—0
Good Automatic Authentication Question Generation—0
Extracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding—0
Collaborative Filtering for Predicting User Preferences for Organizing Objects—0
GPT-4V Takes the Wheel: Promises and Challenges for Pedestrian Behavior Prediction—0
Computational Argumentation: A Journey Beyond Semantics, Logic, Opinions, and Easy Tasks—0
GrapeQA: GRaph Augmentation and Pruning to Enhance Question-Answering—0
Computational principles of intelligence: learning and reasoning with neural networks—0
A Service-Oriented Architecture for Assisting the Authoring of Semantic Crowd Maps—0
Integration of knowledge and data in machine learning—0
Grounding Language Plans in Demonstrations Through Counterfactual Perturbations—0
Interactive and Expressive Code-Augmented Planning with Large Language Models—0
Interpretable Visual Question Answering via Reasoning Supervision—0
Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art—0
Handling Multiword Expressions in Causality Estimation—0
iPerceive: Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering—0
JARVIS: A Neuro-Symbolic Commonsense Reasoning Framework for Conversational Embodied Agents—0
Extended HowNet 2.0 -- An Entity-Relation Common-Sense Representation Model—0
Cluster Flow: how a hierarchical clustering layer make allows deep-NNs more resilient to hacking, more human-like and easily implements relational reasoning—0
HGSGNLP at IEST 2018: An Ensemble of Machine Learning and Deep Neural Architectures for Implicit Emotion Classification in Tweets—0
Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge—0
Hierarchical Relational Inference—0
Consolidating Commonsense Knowledge—0
Constrained Text Generation with Global Guidance -- Case Study on CommonGen—0
Inductive Learning of Answer Set Programs from Noisy Examples—0
A Large Scale Database of Strongly-related Events in Japanese—0
Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems—0
How Pre-trained Word Representations Capture Commonsense Physical Comparisons—0
"Tidy Up the Table": Grounding Common-sense Objective for Tabletop Object Rearrangement—0
How to Understand Named Entities: Using Common Sense for News Captioning—0
HR@JUST Team at SemEval-2020 Task 4: The Impact of RoBERTa Transformer for Evaluation Common Sense Understanding—0
Context-based Natural Language Processing for GIS-based Vague Region Visualization—0
Human-Object Interaction from Human-Level Instructions—0
A Rule-Based Approach to Aspect Extraction from Product Reviews—0
Inducing Neural Models of Script Knowledge—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