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 1–50 of 939 papers

TitleStatusHype
Qwen2.5 Technical ReportCode13
LLaMA: Open and Efficient Foundation Language ModelsCode7
Mamba: Linear-Time Sequence Modeling with Selective State SpacesCode6
GPT-4 Technical ReportCode6
AWQ: Activation-aware Weight Quantization for LLM Compression and AccelerationCode6
Pythia: A Suite for Analyzing Large Language Models Across Training and ScalingCode6
Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsCode6
Training Compute-Optimal Large Language ModelsCode6
Mistral 7BCode6
WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image GenerationCode4
Mixtral of ExpertsCode4
N-Grammer: Augmenting Transformers with latent n-gramsCode4
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-ShotCode4
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelsCode4
Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language ModelsCode4
Cosmos-Reason1: From Physical Common Sense To Embodied ReasoningCode4
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question AnsweringCode4
Galactica: A Large Language Model for ScienceCode4
Gated Delta Networks: Improving Mamba2 with Delta RuleCode4
Knowledge Fusion of Large Language ModelsCode4
MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of ExpertsCode3
ST-MoE: Designing Stable and Transferable Sparse Expert ModelsCode3
Language Models are Few-Shot LearnersCode3
CityWalker: Learning Embodied Urban Navigation from Web-Scale VideosCode3
Common Sense Reasoning for Deepfake DetectionCode3
AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and ReasoningCode3
BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingCode3
Generative agent-based modeling with actions grounded in physical, social, or digital space using ConcordiaCode3
Finetuned Language Models Are Zero-Shot LearnersCode3
Reasoning with Language Model Prompting: A SurveyCode3
OpenFMNav: Towards Open-Set Zero-Shot Object Navigation via Vision-Language Foundation ModelsCode2
On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous DrivingCode2
PaLM: Scaling Language Modeling with PathwaysCode2
Drive Like a Human: Rethinking Autonomous Driving with Large Language ModelsCode2
Easy Problems That LLMs Get WrongCode2
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language ModelsCode2
Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General TasksCode2
DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language ModelsCode2
DeBERTa: Decoding-enhanced BERT with Disentangled AttentionCode2
Deep Bidirectional Language-Knowledge Graph PretrainingCode2
PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about ChangeCode2
LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language ModelsCode2
Large Language Models are Zero-Shot ReasonersCode2
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq ModelCode2
ALBERT: A Lite BERT for Self-supervised Learning of Language RepresentationsCode2
LLM-FP4: 4-Bit Floating-Point Quantized TransformersCode2
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale InstructionsCode2
GPT4RoI: Instruction Tuning Large Language Model on Region-of-InterestCode2
Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and MemoryCode2
Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerCode2
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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