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Logical Reasoning

Papers

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Showing 501–550 of 747 papers

TitleStatusHype
Sinoledge: A Knowledge Engine based on Logical Reasoning and Distributed Micro Services—0
DiLA: Enhancing LLM Tool Learning with Differential Logic Layer—0
Sphere Neural-Networks for Rational Reasoning—0
SPPD: Self-training with Process Preference Learning Using Dynamic Value Margin—0
SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning—0
Standard Neural Computation Alone Is Insufficient for Logical Intelligence—0
STAR: A Benchmark for Situated Reasoning in Real-World Videos—0
Story3D-Agent: Exploring 3D Storytelling Visualization with Large Language Models—0
StructTest: Benchmarking LLMs' Reasoning through Compositional Structured Outputs—0
Structural Embeddings of Tools for Large Language Models—0
Structural Reformation of Large Language Model Neuron Encapsulation for Divergent Information Aggregation—0
SuperCLUE-Fin: Graded Fine-Grained Analysis of Chinese LLMs on Diverse Financial Tasks and Applications—0
SymAgent: A Neural-Symbolic Self-Learning Agent Framework for Complex Reasoning over Knowledge Graphs—0
Symbol Correctness in Deep Neural Networks Containing Symbolic Layers—0
Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach—0
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models—0
System Prompt Poisoning: Persistent Attacks on Large Language Models Beyond User Injection—0
Table-based Fact Verification with Self-adaptive Mixture of Experts—0
Take A Step Back: Rethinking the Two Stages in Visual Reasoning—0
TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving—0
TensorLog: Deep Learning Meets Probabilistic DBs—0
Testing and Evaluation of Large Language Models: Correctness, Non-Toxicity, and Fairness—0
Testing Uncertainty of Large Language Models for Physics Knowledge and Reasoning—0
The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples—0
The General Theory of General Intelligence: A Pragmatic Patternist Perspective—0
The Good, The Bad, and Why: Unveiling Emotions in Generative AI—0
The Multilingual Mind : A Survey of Multilingual Reasoning in Language Models—0
The neural correlates of logical-mathematical symbol systems processing resemble that of spatial cognition more than natural language processing—0
The potential of large language models for improving probability learning: A study on ChatGPT3.5 and first-year computer engineering students—0
The RatioLog Project: Rational Extensions of Logical Reasoning—0
The Society of HiveMind: Multi-Agent Optimization of Foundation Model Swarms to Unlock the Potential of Collective Intelligence—0
The theory of quantitative trading—0
Think Beyond Size: Adaptive Prompting for More Effective Reasoning—0
Thinking Like an Expert:Multimodal Hypergraph-of-Thought (HoT) Reasoning to boost Foundation Modals—0
Time-aware Self-Attention Meets Logic Reasoning in Recommender Systems—0
TimeLogic: A Temporal Logic Benchmark for Video QA—0
Knowledge-based and Data-driven Reasoning and Learning for Ad Hoc Teamwork—0
Towards a Theory of Intentions for Human-Robot Collaboration—0
Towards Better Response Times and Higher-Quality Queries in Interactive Knowledge Base Debugging—0
Towards Competent AI for Fundamental Analysis in Finance: A Benchmark Dataset and Evaluation—0
SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding—0
Towards Generalist Prompting for Large Language Models by Mental Models—0
Towards Human-Compatible XAI: Explaining Data Differentials with Concept Induction over Background Knowledge—0
Towards Ideal Semantics for Analyzing Stream Reasoning—0
Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models—0
Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models—0
Towards Superior Quantization Accuracy: A Layer-sensitive Approach—0
Towards Unifying Logical Entailment and Statistical Estimation—0
Towards Unifying Perceptual Reasoning and Logical Reasoning—0
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning.—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