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Logical Reasoning

Papers

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Showing 301–350 of 747 papers

TitleStatusHype
Improving Multi-hop Logical Reasoning in Knowledge Graphs with Context-Aware Query Representation LearningCode0
Large Language Models are Limited in Out-of-Context Knowledge ReasoningCode0
LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low-Resource and Extinct LanguagesCode0
Flow of Reasoning:Training LLMs for Divergent Problem Solving with Minimal ExamplesCode2
LGR2: Language Guided Reward Relabeling for Accelerating Hierarchical Reinforcement Learning—0
Investigating and Addressing Hallucinations of LLMs in Tasks Involving Negation—0
LogiCode: an LLM-Driven Framework for Logical Anomaly DetectionCode1
On the Hardness of Probabilistic Neurosymbolic LearningCode0
Evaluating the World Model Implicit in a Generative ModelCode2
Bi-Chainer: Automated Large Language Models Reasoning with Bidirectional Chaining—0
How Truncating Weights Improves Reasoning in Language Models—0
Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic Tasks—0
Disentangling Logic: The Role of Context in Large Language Model Reasoning CapabilitiesCode0
A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI—0
Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion—0
Brainstorming Brings Power to Large Language Models of Knowledge Reasoning—0
A Closer Look at Logical Reasoning with LLMs: The Choice of Tool MattersCode0
Easy Problems That LLMs Get WrongCode2
PathReasoner: Modeling Reasoning Path with Equivalent Extension for Logical Question Answering—0
Faithful Logical Reasoning via Symbolic Chain-of-ThoughtCode3
Is a 3D-Tokenized LLM the Key to Reliable Autonomous Driving?—0
RLSF: Reinforcement Learning via Symbolic Feedback—0
Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous DrivingCode2
Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning—0
LLM+Reasoning+Planning for supporting incomplete user queries in presence of APIs—0
STAR: A Benchmark for Situated Reasoning in Real-World Videos—0
MetaReflection: Learning Instructions for Language Agents using Past Reflections—0
MathDivide: Improved mathematical reasoning by large language models—0
Logical Negation Augmenting and Debiasing for Prompt-based Methods—0
Towards a Theoretical Understanding of the 'Reversal Curse' via Training DynamicsCode0
Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning—0
SuperCLUE-Fin: Graded Fine-Grained Analysis of Chinese LLMs on Diverse Financial Tasks and Applications—0
Cantor: Inspiring Multimodal Chain-of-Thought of MLLM—0
Aligning Knowledge Graphs Provided by Humans and Generated from Neural Networks in Specific TasksCode0
LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language ModelsCode1
Reinforcement Learning from Multi-role Debates as Feedback for Bias Mitigation in LLMs—0
MACM: Utilizing a Multi-Agent System for Condition Mining in Solving Complex Mathematical ProblemsCode2
CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues—0
Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding—0
I-Design: Personalized LLM Interior Designer—0
Language Model Guided Interpretable Video Action ReasoningCode0
Advancing LLM Reasoning Generalists with Preference TreesCode3
Classifying Conspiratorial Narratives At Scale: False Alarms and Erroneous ConnectionsCode0
Sphere Neural-Networks for Rational Reasoning—0
LeanReasoner: Boosting Complex Logical Reasoning with LeanCode1
Natural Language as Policies: Reasoning for Coordinate-Level Embodied Control with LLMs—0
Reasoning in Transformers - Mitigating Spurious Correlations and Reasoning Shortcuts—0
Transforming Competition into Collaboration: The Revolutionary Role of Multi-Agent Systems and Language Models in Modern OrganizationsCode0
Learning Guided Automated Reasoning: A Brief Survey—0
Fuzzy Datalog^ over Arbitrary t-Norms—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