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Logical Reasoning

Papers

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Showing 351–400 of 747 papers

TitleStatusHype
Do Large Language Models Truly Grasp Mathematics? An Empirical Exploration From Cognitive Psychology—0
Do Large Language Models Understand Logic or Just Mimick Context?—0
Dynamic In-Context Learning from Nearest Neighbors for Bundle Generation—0
Efficient but Vulnerable: Benchmarking and Defending LLM Batch Prompting Attack—0
Efficient Training and Inference of Hypergraph Reasoning Networks—0
Emergent Symbols through Binding in External Memory—0
Emotion Recognition in Conversation using Probabilistic Soft Logic—0
Empowering LLMs with Logical Reasoning: A Comprehensive Survey—0
Enhanced User Interaction in Operating Systems through Machine Learning Language Models—0
Enhancing Large Language Model Efficiencyvia Symbolic Compression: A Formal Approach Towards Interpretability—0
Enhancing Logical Reasoning in Large Language Models to Facilitate Legal Applications—0
Enhancing Neural Mathematical Reasoning by Abductive Combination with Symbolic Library—0
Enhancing Retrieval Systems with Inference-Time Logical Reasoning—0
Enhancing Transformers for Generalizable First-Order Logical Entailment—0
Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles—0
Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like Biases—0
Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions—0
Evident: a Development Methodology and a Knowledge Base Topology for Data Mining, Machine Learning and General Knowledge Management—0
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence—0
Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic Tasks—0
Exploiting LLMs' Reasoning Capability to Infer Implicit Concepts in Legal Information Retrieval—0
Exploring & Exploiting High-Order Graph Structure for Sparse Knowledge Graph Completion—0
Exploring Generalization Ability of Pretrained Language Models on Arithmetic and Logical Reasoning—0
Extending Automated Deduction for Commonsense Reasoning—0
FaiRR: Faithful and Robust Deductive Reasoning over Natural Language—0
Federated Neural Graph Databases—0
Federated In-Context LLM Agent Learning—0
FEVO: Financial Knowledge Expansion and Reasoning Evolution for Large Language Models—0
Few-shot Visual Reasoning with Meta-analogical Contrastive Learning—0
First Experiments with a Flexible Infrastructure for Normative Reasoning—0
FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts—0
FollowEval: A Multi-Dimensional Benchmark for Assessing the Instruction-Following Capability of Large Language Models—0
uto\!L: Autonomous Evaluation of LLMs for Truth Maintenance and Reasoning Tasks—0
Formal Language Knowledge Corpus for Retrieval Augmented Generation—0
Formal Logic-guided Robust Federated Learning against Poisoning Attacks—0
From Chaos to Order: The Atomic Reasoner Framework for Fine-grained Reasoning in Large Language Models—0
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models—0
From Statistical Relational to Neurosymbolic Artificial Intelligence: a Survey—0
From Statistical Relational to Neuro-Symbolic Artificial Intelligence—0
Fuzzy Datalog^ over Arbitrary t-Norms—0
Generation of Explanations for Logic Reasoning—0
(G)I-DLE: Generative Inference via Distribution-preserving Logit Exclusion with KL Divergence Minimization for Constrained Decoding—0
Graph Collaborative Reasoning—0
GraphIC: A Graph-Based In-Context Example Retrieval Model for Multi-Step Reasoning—0
Graph Neural Networks for Propositional Model Counting—0
Graph Neural Networks for Reasoning 2-Quantified Boolean Formulas—0
Graph Neural Reasoning May Fail in Certifying Boolean Unsatisfiability—0
Guidance is All You Need: Temperature-Guided Reasoning in Large Language Models—0
Handling Noisy Labels via One-Step Abductive Multi-Target Learning and Its Application to Helicobacter Pylori Segmentation—0
Have Large Language Models Learned to Reason? A Characterization via 3-SAT Phase Transition—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