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Logical Reasoning

Papers

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Showing 551–600 of 747 papers

TitleStatusHype
Type-dependent Prompt CycleQAG : Cycle Consistency for Multi-hop Question Generation—0
Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical ReasoningCode1
Neural Methods for Logical Reasoning Over Knowledge GraphsCode1
Towards Human-Compatible XAI: Explaining Data Differentials with Concept Induction over Background Knowledge—0
FOLIO: Natural Language Reasoning with First-Order LogicCode1
Time-aware Self-Attention Meets Logic Reasoning in Recommender Systems—0
Knowledge-based and Data-driven Reasoning and Learning for Ad Hoc Teamwork—0
A Scalable, Interpretable, Verifiable & Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables—0
Reduced Implication-bias Logic Loss for Neuro-Symbolic Learning—0
Emotion Recognition in Conversation using Probabilistic Soft Logic—0
Language models show human-like content effects on reasoning tasksCode0
Discourse-Aware Graph Networks for Textual Logical Reasoning—0
AnaLog: Testing Analytical and Deductive Logic Learnability in Language Models—0
Learning Symmetric Rules with SATNetCode0
Towards Unifying Perceptual Reasoning and Logical Reasoning—0
Semantic Probabilistic Layers for Neuro-Symbolic LearningCode1
TAR: Neural Logical Reasoning across TBox and ABox—0
TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge GraphCode1
Reasoning over Logically Interacted Conditions for Question Answering—0
RobustLR: Evaluating Robustness to Logical Perturbation in Deductive ReasoningCode0
Large Language Models are Zero-Shot ReasonersCode2
On the Paradox of Learning to Reason from DataCode1
Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI ModelsCode0
FLEX: Feature-Logic Embedding Framework for CompleX Knowledge Graph ReasoningCode0
Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning—0
LogiGAN: Learning Logical Reasoning via Adversarial Pre-training—0
Graph Neural Networks for Propositional Model Counting—0
Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical ReasoningCode1
Table-based Fact Verification with Self-adaptive Mixture of ExpertsCode0
Reasoning with Multi-Structure Commonsense Knowledge in Visual Dialog—0
PaLM: Scaling Language Modeling with PathwaysCode2
Training Compute-Optimal Large Language ModelsCode6
Enhancing Neural Mathematical Reasoning by Abductive Combination with Symbolic Library—0
A Densely Connected Criss-Cross Attention Network for Document-level Relation Extraction—0
AbductionRules: Training Transformers to Explain Unexpected InputsCode1
A Neural-Symbolic Approach to Natural Language UnderstandingCode0
ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical ReasoningCode2
FaiRR: Faithful and Robust Deductive Reasoning over Natural LanguageCode1
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading ComprehensionCode1
What Makes Reading Comprehension Questions Difficult?Code0
A Neuro-vector-symbolic Architecture for Solving Raven's Progressive MatricesCode1
MERIt: Meta-Path Guided Contrastive Learning for Logical ReasoningCode1
Towards Unifying Logical Entailment and Statistical Estimation—0
MUC-driven Feature Importance Measurement and Adversarial Analysis for Random Forest—0
JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning—0
ExAIS: Executable AI SemanticsCode1
End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without OverthinkingCode1
Logical Reasoning for Task Oriented Dialogue Systems—0
VAEL: Bridging Variational Autoencoders and Probabilistic Logic ProgrammingCode1
Neural Logic Analogy Learning—0
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All datasetsLingOlyBIG-bench (Formal Fallacies Syllogisms Negation)BIG-bench (Penguins In A Table)BIG-bench (Reasoning About Colored Objects)BIG-bench (Temporal Sequences)BIG-bench (Logic Grid Puzzle)BIG-bench (StrategyQA)RuWorldTreeWinograd AutomaticBIG-bench (Logical Fallacy Detection)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Claude OpusDelta_NoContext28.8—Unverified
2GPT-4oDelta_NoContext25.1—Unverified
3Gemini 1.5 ProDelta_NoContext23.4—Unverified
4GPT-4Delta_NoContext21.5—Unverified
5Command R+Delta_NoContext11.6—Unverified
6GPT-3.5Delta_NoContext11.2—Unverified
7Mixtral 8x7BDelta_NoContext6.4—Unverified
8Llama 3 8BDelta_NoContext4.9—Unverified
9Llama 3 70BDelta_NoContext2.9—Unverified
10Gemma 7BDelta_NoContext2.2—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLM 2 (few-shot, k=3, Direct)Accuracy64.8—Unverified
2PaLM 2 (few-shot, k=3, CoT)Accuracy57.2—Unverified
3OPT 66B (few-shot, k=3)Accuracy54—Unverified
4PaLM 540B (few-shot, k=3)Accuracy53.6—Unverified
5GPT-NeoX 20B (few-shot, k=3)Accuracy52.8—Unverified
6BLOOM 176B (few-shot, k=3)Accuracy52.8—Unverified
7Chinchilla-70B (few-shot, k=5)Accuracy52.1—Unverified
8Bloomberg GPT 50B (few-shot, k=3)Accuracy50.8—Unverified
9Gopher-280B (few-shot, k=5)Accuracy50.7—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLM 2 (few-shot, k=3, CoT)Accuracy84.9—Unverified
2PaLM 2 (few-shot, k=3, Direct)Accuracy65.8—Unverified
3Chinchilla-70B (few-shot, k=5)Accuracy48.7—Unverified
4PaLM 540B (few-shot, k=3)Accuracy44.5—Unverified
5Gopher-280B (few-shot, k=5)Accuracy40.6—Unverified
6BLOOM 176B (few-shot, k=3)Accuracy40.41—Unverified
7Bloomberg GPT (few-shot, k=3)Accuracy37.67—Unverified
8GPT-NeoX (few-shot, k=3)Accuracy33.56—Unverified
9OPT 66B (few-shot, k=3)Accuracy28.08—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLM 2 (few-shot, k=3, CoT)Accuracy91.2—Unverified
2PaLM 2 (few-shot, k=3, Direct)Accuracy61.2—Unverified
3Chinchilla-70B (few-shot, k=5)Accuracy59.7—Unverified
4Gopher-280B (few-shot, k=5)Accuracy49.2—Unverified
5PaLM 540B (few-shot, k=3)Accuracy38—Unverified
6BLOOM 176B (few-shot, k=3)Accuracy36.8—Unverified
7Bloomberg GPT (few-shot, k=3)Accuracy34.8—Unverified
8OPT 66B (few-shot, k=3)Accuracy31.2—Unverified
9GPT-NeoX (few-shot, k=3)Accuracy26—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLM 2 (few-shot, k=3, CoT)Accuracy100—Unverified
2PaLM 2 (few-shot, k=3, Direct)Accuracy96.4—Unverified
3PaLM 540B (few-shot, k=3)Accuracy39.6—Unverified
4BLOOM 176B (few-shot, k=3)Accuracy36.8—Unverified
5Chinchilla-70B (few-shot, k=5)Accuracy32—Unverified
6Bloomberg GPT (few-shot, k=3)Accuracy29.2—Unverified
7OPT 66B (few-shot, k=3)Accuracy23.6—Unverified
8GPT-NeoX (few-shot, k=3)Accuracy21.2—Unverified
9Gopher-280B (few-shot, k=5)Accuracy19—Unverified
#ModelMetricClaimedVerifiedStatus
1Chinchilla-70B (few-shot, k=5)Accuracy44—Unverified
2PaLM-540B (few-shot, k=5)Accuracy42.4—Unverified
3PaLM-62B (few-shot, k=5)Accuracy36.5—Unverified
4Gopher-280B (few-shot, k=5)Accuracy35.1—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLM-540B (few-shot, k=5)Accuracy73.9—Unverified
2Chinchilla-70B (few-shot, k=5)Accuracy68.3—Unverified
3PaLM-62B (few-shot, k=5)Accuracy65.4—Unverified
4Gopher-280B (few-shot, k=5)Accuracy61—Unverified
#ModelMetricClaimedVerifiedStatus
1Human benchmarkAccuracy 83.7—Unverified
2RuGPT-3 LargeAccuracy 40.7—Unverified
3RuGPT-3 MediumAccuracy 38—Unverified
4RuGPT-3 SmallAccuracy 34—Unverified
#ModelMetricClaimedVerifiedStatus
1Human benchmarkAccuracy87—Unverified
2RuGPT-3 SmallAccuracy57.9—Unverified
3RuGPT-3 MediumAccuracy57.2—Unverified
4RuGPT-3 LargeAccuracy55.5—Unverified
#ModelMetricClaimedVerifiedStatus
1Chinchilla-70B (few-shot, k=5)Accuracy72.1—Unverified
2Gopher-280B (few-shot, k=5)Accuracy58.9—Unverified