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 151–175 of 939 papers

TitleStatusHype
DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented GenerationCode1
Global-Local Tree Search in VLMs for 3D Indoor Scene GenerationCode1
Do Multilingual Language Models Think Better in English?Code1
Boosting Theory-of-Mind Performance in Large Language Models via PromptingCode1
Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot LearnersCode1
Comprehensive Visual Question Answering on Point Clouds through Compositional Scene ManipulationCode1
A Semantic Space is Worth 256 Language Descriptions: Make Stronger Segmentation Models with Descriptive PropertiesCode1
Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained TransformersCode1
Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World KnowledgeCode1
DialogSum: A Real-Life Scenario Dialogue Summarization DatasetCode1
Does It Make Sense? And Why? A Pilot Study for Sense Making and ExplanationCode1
Image Generation From Small Datasets via Batch Statistics AdaptationCode1
ConceptBert: Concept-Aware Representation for Visual Question AnsweringCode1
Mitigating the Alignment Tax of RLHFCode1
KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational GraphsCode1
Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense ReasoningCode1
Conversational Word Embedding for Retrieval-Based Dialog SystemCode1
Fusing Context Into Knowledge Graph for Commonsense Question AnsweringCode1
Common Sense Enhanced Knowledge-based Recommendation with Large Language ModelCode1
Language Models are Unsupervised Multitask LearnersCode1
How Can We Know When Language Models Know? On the Calibration of Language Models for Question AnsweringCode1
Large Language Models are Better Reasoners with Self-VerificationCode1
Large Language Models as Evaluators for Recommendation ExplanationsCode1
Layout-aware Dreamer for Embodied Referring Expression GroundingCode1
Multi-Modal Grounded Planning and Efficient Replanning For Learning Embodied Agents with A Few ExamplesCode1
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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