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 51–100 of 939 papers

TitleStatusHype
KnowZRel: Common Sense Knowledge-based Zero-Shot Relationship Retrieval for Generalised Scene Graph GenerationCode0
PredictaBoard: Benchmarking LLM Score PredictabilityCode0
Navigating Semantic Relations: Challenges for Language Models in Abstract Common-Sense Reasoning—0
Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning—0
Tell Me Why: Incentivizing Explanations—0
Inference-Time Computations for LLM Reasoning and Planning: A Benchmark and Insights—0
Plant in Cupboard, Orange on Rably, Inat Aphone. Benchmarking Incremental Learning of Situation and Language Model using a Text-Simulated Situated Environment—0
ViRAC: A Vision-Reasoning Agent Head Movement Control Framework in Arbitrary Virtual Environments—0
Elucidation of the Concept of Consciousness from the Theory of Non-Human Communication Agents—0
Large Language Models as Common-Sense Heuristics—0
MACI: Multi-Agent Collaborative Intelligence for Adaptive Reasoning and Temporal Planning—0
PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding—0
Large Language Models as Theory of Mind Aware Generative Agents with Counterfactual Reflection—0
Towards A Litmus Test for Common Sense—0
A note on bequest preferences in utility maximisation for modern tontines—0
The Quest for Visual Understanding: A Journey Through the Evolution of Visual Question Answering—0
Common Sense Is All You Need—0
MSWA: Refining Local Attention with Multi-ScaleWindow Attention—0
FirePlace: Geometric Refinements of LLM Common Sense Reasoning for 3D Object Placement—0
DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery—0
KnowRA: Knowledge Retrieval Augmented Method for Document-level Relation Extraction with Comprehensive Reasoning Abilities—0
Titans: Learning to Memorize at Test TimeCode0
Embodied Image Quality Assessment for Robotic IntelligenceCode0
VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning Tasks—0
Multi-Modal Grounded Planning and Efficient Replanning For Learning Embodied Agents with A Few ExamplesCode1
Qwen2.5 Technical ReportCode13
QUENCH: Measuring the gap between Indic and Non-Indic Contextual General Reasoning in LLMsCode0
A Multimodal Social Agent—0
The Rosetta Paradox: Domain-Specific Performance Inversions in Large Language Models—0
Gated Delta Networks: Improving Mamba2 with Delta RuleCode4
A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions—0
Rethinking Annotation for Object Detection: Is Annotating Small-size Instances Worth Its Cost?—0
A surprisal oracle for when every layer countsCode0
Let's Think Var-by-Var: Large Language Models Enable Ad Hoc Probabilistic Reasoning—0
MALT: Improving Reasoning with Multi-Agent LLM Training—0
Online Knowledge Integration for 3D Semantic Mapping: A Survey—0
CityWalker: Learning Embodied Urban Navigation from Web-Scale VideosCode3
HEIE: MLLM-Based Hierarchical Explainable AIGC Image Implausibility Evaluator—0
Generating Out-Of-Distribution Scenarios Using Language Models—0
Interactive and Expressive Code-Augmented Planning with Large Language Models—0
GLOVER: Generalizable Open-Vocabulary Affordance Reasoning for Task-Oriented Grasping—0
Improving Tool Retrieval by Leveraging Large Language Models for Query Generation—0
Knowledge Bases in Support of Large Language Models for Processing Web News—0
CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision—0
A little less conversation, a little more action, please: Investigating the physical common-sense of LLMs in a 3D embodied environmentCode0
Diffusion as Reasoning: Enhancing Object Goal Navigation with LLM-Biased Diffusion Model—0
Language Agents Meet Causality -- Bridging LLMs and Causal World ModelsCode1
IPPON: Common Sense Guided Informative Path Planning for Object Goal Navigation—0
From Blind Solvers to Logical Thinkers: Benchmarking LLMs' Logical Integrity on Faulty Mathematical Problems—0
Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving—0
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All datasetsWinoGrandearc_challengearc_easyReCoRDCommonsenseQAPARusRuCoSRWSDBIG-bench (Causal Judgment)BIG-bench (Date Understanding)BIG-bench (Disambiguation QA)BIG-bench (Sports Understanding)

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